------------------------------------------------------------------------------
      name:  <unnamed>
       log:  c:\dropbox\data\potatoes\data archive restat\testingreplication.l
> og
  log type:  text
 opened on:   3 May 2017, 21:50:31

. 
. *******************Table 1: Potato Cultivation & Sales by Sample Farmers, 20
> 08
. use analysisdata, clear

. 
. //Potato cultivation & allocation
. tabstat Totarea Totharv Pctgs Pcths Pctcs Pctsp Qtysold Pctmkt Pctphmlda Tot
> rev Netrev if utag_all==1, stats(mean semean) columns(statistics)

    variable |      mean  se(mean)
-------------+--------------------
     Totarea |  .6630526  .0170461
     Totharv |  6553.344  177.2059
       Pctgs |  .4283659  .0093292
       Pcths |  .1645429  .0070148
       Pctcs |  .2846551   .007791
       Pctsp |  .0261747  .0014075
     Qtysold |   5962.62  184.4841
      Pctmkt |  .0785714  .0063513
   Pctphmlda |  .9084248  .0067972
      Totrev |  12887.16  413.0189
      Netrev |  11974.72  364.5925
----------------------------------

. 
. //The mandi price
. use mandiprices, clear

. collapse (mean) mandipx if (variety==1 | variety==2), by(mv_mktgroup year ha
> rvest)

. 
. tabstat mandipx if year==2008, statistics(mean semean)

    variable |      mean  se(mean)
-------------+--------------------
     mandipx |    4.8206   .160015
----------------------------------

. 
. ******Row 1 in Table A1: Traders sold at
. tabstat mandipx if year==2008, by(harvest) statistics(mean semean)

Summary for variables: mandipx
     by categories of: harvest 

 harvest |      mean  se(mean)
---------+--------------------
       0 |  4.834321  .2655386
       1 |  4.806879  .1852812
---------+--------------------
   Total |    4.8206   .160015
------------------------------

. 
. //The tracked price
. use information2008data, clear

. tabstat I2_wholeprice if (variety==1 | variety==2) & intvn==1, stats(mean se
> mean)

    variable |      mean  se(mean)
-------------+--------------------
I2_wholepr~e |  2.672434  .0080812
----------------------------------

. 
. *******************Table 2: Pass-through
. use mandiprices, clear

. 
. collapse (count) numvillages=mzcode (mean) mandipx=mandipx farmgatepx=meanfa
> rmgatepx retailpx=retailprice meanyield=meanyield retailprice market v_wagec
> ashmale d=d dscode=dscode (sum) totarea=totarea tothhs=v_numofhhs totlandlin
> ehhs=v_landlinehhs totcanals=v_numofcanals tottubewells=v_numoftubewells tot
> puccaroad=puccaroad totindmill=industrymill bank=bank, by(mv_mktgroup year w
> eek)

. 
. gen pctlandline=totlandlinehhs/tothhs

. gen canalsphh=totcanals/tothhs

. gen tubewellsphh=tottubewells/tothhs

. gen puccaroadpv=totpuccaroad/numvillages

. gen indmillpv=totindmill/numvillages

. gen bankphh=bank/tothhs

. gen medpur=(dscode==2)

. gen dxretail=d*retailprice
(264 missing values generated)

. gen totoutput=meanyield*totarea

. replace meanyield=meanyield/1000
(4177 real changes made)

. replace d=d/100
(4177 real changes made)

. 
. gen harvest=(week>0 & week<=12)

. gen postharvearly=(week>12 & week<=26)

. gen postharvlate=(week>26 & week<=52)

. 
. ///Regressions for mandi price
> areg mandipx retailprice meanyield pctlandline puccaroadpv indmillpv i.week 
> i.year if harvest==0, absorb(mv_mktgroup) robust
note: pctlandline omitted because of collinearity
note: puccaroadpv omitted because of collinearity
note: indmillpv omitted because of collinearity

Linear regression, absorbing indicators           Number of obs   =       2691
                                                  F(  44,   2624) =    2647.98
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.9773
                                                  Adj R-squared   =     0.9767
                                                  Root MSE        =     0.4440

------------------------------------------------------------------------------
             |               Robust
     mandipx |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 retailprice |   .8090885   .0089191    90.71   0.000     .7915994    .8265777
   meanyield |  -.0299932   .0196998    -1.52   0.128    -.0686219    .0086356
 pctlandline |          0  (omitted)
 puccaroadpv |          0  (omitted)
   indmillpv |          0  (omitted)
             |
        week |
         14  |   .3290214   .0947256     3.47   0.001     .1432771    .5147658
         15  |   .1380595   .0763943     1.81   0.071    -.0117397    .2878587
         16  |    .271249   .0805546     3.37   0.001     .1132921     .429206
         17  |    .301994   .0851989     3.54   0.000     .1349301    .4690579
         18  |   .2337712    .087276     2.68   0.007     .0626344     .404908
         19  |   .2815378   .0950535     2.96   0.003     .0951503    .4679253
         20  |   .1798497   .0992823     1.81   0.070    -.0148298    .3745291
         21  |   .3928724   .0774849     5.07   0.000     .2409347    .5448101
         22  |   .5226077    .073886     7.07   0.000      .377727    .6674885
         23  |   .4949176   .0674708     7.34   0.000     .3626163    .6272188
         24  |   .4915863   .0664147     7.40   0.000     .3613558    .6218167
         25  |   .5750821   .0686611     8.38   0.000     .4404466    .7097176
         26  |   .5516119   .0656066     8.41   0.000      .422966    .6802579
         27  |   .6121822   .0667759     9.17   0.000     .4812435    .7431209
         28  |   .6940011    .064161    10.82   0.000     .5681898    .8198124
         29  |   .8430279   .0608075    13.86   0.000     .7237924    .9622633
         30  |   .7583352   .0657039    11.54   0.000     .6294985     .887172
         31  |   .7027702    .062676    11.21   0.000      .579871    .8256695
         32  |   .4972327   .0631821     7.87   0.000     .3733409    .6211244
         33  |   .5800712   .0606247     9.57   0.000     .4611941    .6989484
         34  |    .662534   .0653552    10.14   0.000     .5343811    .7906868
         35  |   .6449435   .0616132    10.47   0.000      .524128    .7657589
         36  |   .5463013   .0614246     8.89   0.000     .4258558    .6667468
         37  |    .546802   .0622892     8.78   0.000     .4246612    .6689429
         38  |   .4777357   .0645285     7.40   0.000     .3512037    .6042677
         39  |   .7060835   .0591776    11.93   0.000     .5900441     .822123
         40  |   .6048353   .0687752     8.79   0.000      .469976    .7396945
         41  |   .6011402   .0754955     7.96   0.000     .4531034     .749177
         42  |   .5846446   .0776982     7.52   0.000     .4322888    .7370005
         43  |   .5552193   .0786532     7.06   0.000     .4009906     .709448
         44  |   .4888556   .0733315     6.67   0.000     .3450622    .6326489
         45  |   .6188041   .0724369     8.54   0.000     .4767649    .7608433
         46  |   .5888988   .0731915     8.05   0.000     .4453799    .7324176
         47  |   .6252701   .0911267     6.86   0.000     .4465825    .8039577
         48  |   .8232363   .1092129     7.54   0.000     .6090841    1.037388
         49  |   .4282438   .1704715     2.51   0.012     .0939716     .762516
         50  |   .2073871   .1565631     1.32   0.185    -.0996125    .5143866
         51  |   .1267641   .1458036     0.87   0.385    -.1591376    .4126657
         52  |   -.160808   .1310212    -1.23   0.220    -.4177233    .0961074
             |
        year |
       2008  |   .4009261   .0673863     5.95   0.000     .2687905    .5330617
       2011  |   1.383922   .0829694    16.68   0.000      1.22123    1.546614
       2012  |   2.253781   .0731596    30.81   0.000     2.110325    2.397238
             |
       _cons |  -.5871989   .1849921    -3.17   0.002    -.9499441   -.2244536
-------------+----------------------------------------------------------------
 mv_mktgroup |   absorbed                                      (23 categories)

. 
. areg mandipx retailprice i.week if year==2008 & harvest==0, absorb(mv_mktgro
> up) robust

Linear regression, absorbing indicators           Number of obs   =        790
                                                  F(  36,    732) =      42.61
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.9129
                                                  Adj R-squared   =     0.9061
                                                  Root MSE        =     0.4258

------------------------------------------------------------------------------
             |               Robust
     mandipx |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 retailprice |   .6632445   .0481664    13.77   0.000     .5686837    .7578053
             |
        week |
         14  |   .2819471    .113952     2.47   0.014     .0582354    .5056589
         15  |    .185238   .1180285     1.57   0.117    -.0464767    .4169528
         16  |   .4526781   .1337952     3.38   0.001     .1900101    .7153462
         17  |    .461132   .1234761     3.73   0.000     .2187225    .7035414
         18  |   .4526681   .1383253     3.27   0.001     .1811065    .7242296
         19  |   .6178151   .1892385     3.26   0.001     .2463001    .9893301
         20  |   .6091205   .2095879     2.91   0.004     .1976554    1.020586
         21  |   .6259524   .1488086     4.21   0.000     .3338098     .918095
         22  |   .5689892   .1389968     4.09   0.000     .2961092    .8418692
         23  |   .5305548   .1177489     4.51   0.000      .299389    .7617205
         24  |   .6814937   .0982668     6.94   0.000     .4885753    .8744122
         25  |   .6312121   .1081334     5.84   0.000     .4189235    .8435008
         26  |   .5456946   .1071043     5.09   0.000     .3354264    .7559629
         27  |    .644189   .0895895     7.19   0.000     .4683059    .8200721
         28  |   .5876426   .1047478     5.61   0.000     .3820006    .7932846
         29  |   .8004615   .0917158     8.73   0.000     .6204041     .980519
         30  |   .6261398   .0929896     6.73   0.000     .4435817    .8086978
         31  |    .590231    .082244     7.18   0.000     .4287689    .7516932
         32  |   .5420626   .0763791     7.10   0.000     .3921145    .6920108
         33  |   .4701332   .0758657     6.20   0.000     .3211929    .6190736
         34  |   .4773006    .081424     5.86   0.000     .3174482    .6371531
         35  |   .4571581   .0812384     5.63   0.000     .2976701    .6166461
         36  |   .5343997   .0838208     6.38   0.000     .3698419    .6989575
         37  |   .5038257   .0808312     6.23   0.000     .3451371    .6625143
         38  |   .2654372   .1168561     2.27   0.023     .0360241    .4948503
         39  |    .548988    .082858     6.63   0.000     .3863205    .7116556
         40  |   .4740314   .0897528     5.28   0.000     .2978279    .6502349
         41  |   .4830735   .1360003     3.55   0.000     .2160764    .7500706
         42  |    .456201    .125906     3.62   0.000      .209021     .703381
         43  |    .490704   .1169265     4.20   0.000     .2611527    .7202552
         44  |   .5866675   .1309445     4.48   0.000      .329596     .843739
         45  |   .4755819       .139     3.42   0.001     .2026957     .748468
         46  |   .7367978   .1528067     4.82   0.000     .4368062    1.036789
         47  |   .9508907   .2043714     4.65   0.000     .5496667    1.352115
         48  |   1.103672   .2135919     5.17   0.000     .6843466    1.522998
             |
       _cons |   .3457441    .244641     1.41   0.158    -.1345375    .8260257
-------------+----------------------------------------------------------------
 mv_mktgroup |   absorbed                                      (22 categories)

. 
. //Regressions for farmgate price: this can only be run for 2008
. areg farmgatepx retailprice meanyield i.week if harvest==0, absorb(mv_mktgro
> up) robust
note: meanyield omitted because of collinearity
note: 47.week omitted because of collinearity
note: 49.week omitted because of collinearity
note: 50.week omitted because of collinearity
note: 51.week omitted because of collinearity
note: 52.week omitted because of collinearity

Linear regression, absorbing indicators           Number of obs   =        596
                                                  F(  35,    539) =      14.50
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5300
                                                  Adj R-squared   =     0.4811
                                                  Root MSE        =     0.4719

------------------------------------------------------------------------------
             |               Robust
  farmgatepx |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 retailprice |   .0230792   .0675312     0.34   0.733    -.1095774    .1557359
   meanyield |          0  (omitted)
             |
        week |
         14  |   .5436631    .134771     4.03   0.000     .2789223     .808404
         15  |   .4863419   .1532445     3.17   0.002     .1853121    .7873716
         16  |   .8378677   .1717771     4.88   0.000      .500433    1.175302
         17  |   .8791833   .1779957     4.94   0.000      .529533    1.228834
         18  |   .7948544   .1625689     4.89   0.000     .4755081    1.114201
         19  |   .7191839   .2017749     3.56   0.000     .3228224    1.115545
         20  |     .46541   .2228084     2.09   0.037     .0277307    .9030893
         21  |   .8344272   .2418257     3.45   0.001      .359391    1.309463
         22  |   .4267938    .208495     2.05   0.041     .0172315    .8363561
         23  |   .2023242   .1689427     1.20   0.232    -.1295427    .5341911
         24  |  -.0349552   .1939038    -0.18   0.857     -.415855    .3459447
         25  |   .1214945   .1704141     0.71   0.476    -.2132626    .4562516
         26  |   .0375904   .2299514     0.16   0.870    -.4141204    .4893012
         27  |   .5359625   .1673941     3.20   0.001     .2071377    .8647873
         28  |   .1076478   .2019711     0.53   0.594    -.2890991    .5043948
         29  |   .3213699   .2106386     1.53   0.128    -.0924033    .7351431
         30  |  -.0992898   .1695179    -0.59   0.558    -.4322865     .233707
         31  |  -.0021781   .1767058    -0.01   0.990    -.3492945    .3449384
         32  |   .1965026   .1625872     1.21   0.227    -.1228797    .5158848
         33  |   -.285856   .1491872    -1.92   0.056    -.5789156    .0072036
         34  |  -.0328715   .1833125    -0.18   0.858    -.3929661     .327223
         35  |   .0315169   .2366871     0.13   0.894    -.4334253    .4964591
         36  |   .0142214   .1511101     0.09   0.925    -.2826156    .3110584
         37  |   .0236038    .152606     0.15   0.877    -.2761716    .3233791
         38  |   .1393894   .1754061     0.79   0.427     -.205174    .4839528
         39  |   -.046605   .2003179    -0.23   0.816    -.4401046    .3468945
         40  |    .026207   .1545619     0.17   0.865    -.2774105    .3298246
         41  |  -.1649175   .1739856    -0.95   0.344    -.5066905    .1768555
         42  |  -.1537431   .1557972    -0.99   0.324    -.4597873    .1523011
         43  |  -.1810242   .1832088    -0.99   0.324     -.540915    .1788666
         44  |   .3012024   .1929513     1.56   0.119    -.0778263    .6802311
         45  |   .1736889   .1829242     0.95   0.343    -.1856429    .5330206
         46  |   .1948259   .1984195     0.98   0.327    -.1949445    .5845962
         47  |          0  (empty)
         48  |  -.5508204   .1866237    -2.95   0.003    -.9174193   -.1842216
         49  |          0  (empty)
         50  |          0  (empty)
         51  |          0  (empty)
         52  |          0  (empty)
             |
       _cons |   1.768097   .3417508     5.17   0.000     1.096771    2.439424
-------------+----------------------------------------------------------------
 mv_mktgroup |   absorbed                                      (22 categories)

. 
. //Regression for farmgate price on mandi price: this can only be run for 200
> 8
. areg farmgatepx mandipx meanyield i.week if harvest==0, absorb(mv_mktgroup) 
> robust
note: meanyield omitted because of collinearity
note: 47.week omitted because of collinearity
note: 49.week omitted because of collinearity
note: 50.week omitted because of collinearity
note: 51.week omitted because of collinearity
note: 52.week omitted because of collinearity

Linear regression, absorbing indicators           Number of obs   =        596
                                                  F(  35,    539) =      13.57
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5306
                                                  Adj R-squared   =     0.4819
                                                  Root MSE        =     0.4716

------------------------------------------------------------------------------
             |               Robust
  farmgatepx |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     mandipx |   .0426504   .0481824     0.89   0.376    -.0519979    .1372986
   meanyield |          0  (omitted)
             |
        week |
         14  |   .5311101   .1358591     3.91   0.000     .2642318    .7979883
         15  |   .4736404   .1448133     3.27   0.001     .1891728    .7581081
         16  |   .8122001   .1612485     5.04   0.000     .4954476    1.128953
         17  |   .8526126   .1751098     4.87   0.000     .5086313    1.196594
         18  |   .7704326   .1593909     4.83   0.000      .457329    1.083536
         19  |   .6850674   .1799472     3.81   0.000     .3315837    1.038551
         20  |   .4374871   .2005954     2.18   0.030     .0434426    .8315317
         21  |   .8012566    .223838     3.58   0.000     .3615548    1.240958
         22  |   .3942907   .1950486     2.02   0.044      .011142    .7774393
         23  |   .1732675    .155138     1.12   0.265    -.1314817    .4780167
         24  |  -.0692865   .1928989    -0.36   0.720    -.4482123    .3096393
         25  |   .0879527   .1841311     0.48   0.633    -.2737497    .4496551
         26  |   .0131278   .2212179     0.06   0.953    -.4214272    .4476827
         27  |   .5051253   .1747943     2.89   0.004     .1617637    .8484868
         28  |     .07537   .2054864     0.37   0.714    -.3282824    .4790225
         29  |   .2832757   .2109105     1.34   0.180    -.1310316    .6975831
         30  |   -.126845   .1670608    -0.76   0.448    -.4550151    .2013251
         31  |  -.0301341   .1783124    -0.17   0.866    -.3804065    .3201383
         32  |   .1720267    .165204     1.04   0.298     -.152496    .4965494
         33  |  -.3081149    .151542    -2.03   0.043    -.6058001   -.0104296
         34  |  -.0570628   .1844205    -0.31   0.757    -.4193338    .3052082
         35  |   .0088118   .2372901     0.04   0.970     -.457315    .4749386
         36  |  -.0107621   .1558048    -0.07   0.945    -.3168212     .295297
         37  |  -.0002652   .1558423    -0.00   0.999    -.3063979    .3058676
         38  |   .1203665   .1822101     0.66   0.509    -.2375624    .4782955
         39  |  -.0730489   .2004327    -0.36   0.716    -.4667738     .320676
         40  |   .0027232   .1539438     0.02   0.986    -.2996801    .3051266
         41  |  -.1935833   .1550227    -1.25   0.212     -.498106    .1109395
         42  |  -.1799557   .1439766    -1.25   0.212    -.4627798    .1028684
         43  |  -.2076303   .1767133    -1.17   0.241    -.5547613    .1395008
         44  |   .2690662    .192333     1.40   0.162     -.108748    .6468804
         45  |   .1451768   .1690379     0.86   0.391    -.1868771    .4772307
         46  |   .1544101   .1920766     0.80   0.422    -.2229003    .5317205
         47  |          0  (empty)
         48  |  -.6079572   .1819001    -3.34   0.001    -.9652773   -.2506372
         49  |          0  (empty)
         50  |          0  (empty)
         51  |          0  (empty)
         52  |          0  (empty)
             |
       _cons |   1.727083   .2044216     8.45   0.000     1.325523    2.128644
-------------+----------------------------------------------------------------
 mv_mktgroup |   absorbed                                      (22 categories)

. 
. *******************Table 3: Effect of Interventions on Farmers' Price Tracki
> ng Behavior & Precision
. use information2008data, clear

. 
. rename i2_wholeprice trackedpx

. 
. gen nophonecontrol=(intvn==2 & phone==0)

. replace nophonecontrol=. if (intvn==3 | phone==1)
(5886 real changes made, 5886 to missing)

. gen withphonecontrol=(intvn==2 & phone==1)

. replace withphonecontrol=. if (intvn==3 | (intvn==2 & phone==0))
(8502 real changes made, 8502 to missing)

. gen withphonenophone=(phone==1)

. replace withphonenophone=. if (intvn==1 | intvn==3)
(9263 real changes made, 9263 to missing)

. 
. gen sourceother=(sourceofinfo_whole==3)

. replace sourceother=. if sourceofinfo_whole==.
(1691 real changes made, 1691 to missing)

. 
. *****Panel A: Effect of Information on Price Tracking
. //Do you track wholesale prices?
. logit i2_trackwhole int2 phone int3 own2008 i.variety medpur i.month if (var
> iety==1 | variety==2) & uvmtag==1, or clus(mzid)

Iteration 0:   log pseudolikelihood = -4456.3035  
Iteration 1:   log pseudolikelihood = -3861.6253  
Iteration 2:   log pseudolikelihood = -3748.6558  
Iteration 3:   log pseudolikelihood = -3746.7915  
Iteration 4:   log pseudolikelihood = -3746.7898  
Iteration 5:   log pseudolikelihood = -3746.7898  

Logistic regression                               Number of obs   =      11719
                                                  Wald chi2(14)   =      92.76
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -3746.7898                 Pseudo R2       =     0.1592

                                  (Std. Err. adjusted for 72 clusters in mzid)
------------------------------------------------------------------------------
             |               Robust
i2_trackwh~e | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        int2 |   .8047907   .3778478    -0.46   0.644     .3206604    2.019857
       phone |   1.818106    .549303     1.98   0.048     1.005649    3.286943
        int3 |   8.595671    5.69581     3.25   0.001     2.345536     31.5005
     own2008 |   1.578105   .2091811     3.44   0.001     1.217047    2.046277
   2.variety |    1.47295   .4095533     1.39   0.164     .8541057    2.540179
      medpur |   .7228179   .3013864    -0.78   0.436     .3192358    1.636614
             |
       month |
          4  |   .7816263   .3029586    -0.64   0.525     .3656563    1.670803
          5  |   .9532187   .4764262    -0.10   0.924     .3578948    2.538808
          6  |   .4624202    .200944    -1.77   0.076     .1973097     1.08374
          7  |   .3812878     .17876    -2.06   0.040     .1521186    .9557044
          8  |   .2908761   .1381557    -2.60   0.009     .1146615    .7379013
          9  |   .2629595   .1253103    -2.80   0.005     .1033377    .6691431
         10  |   .2659405   .1356306    -2.60   0.009     .0978743    .7226037
         11  |   .2712593   .1525033    -2.32   0.020     .0901229    .8164586
             |
       _cons |   8.197246   4.431032     3.89   0.000     2.841514    23.64755
------------------------------------------------------------------------------

. 
. //Minimum number of days ago that the price was tracked
. poisson minnumdays_whole int2 phone int3 own2008 i.variety medpur i.month if
>  (variety==1 | variety==2) & uvmtag==1, irr clus(mzid)

Iteration 0:   log pseudolikelihood = -20542.099  
Iteration 1:   log pseudolikelihood = -20542.098  

Poisson regression                                Number of obs   =      10267
                                                  Wald chi2(14)   =      71.77
Log pseudolikelihood = -20542.098                 Prob > chi2     =     0.0000

                                  (Std. Err. adjusted for 72 clusters in mzid)
------------------------------------------------------------------------------
             |               Robust
minnumdays~e |        IRR   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        int2 |   .6916453   .0694091    -3.67   0.000     .5681491    .8419853
       phone |   .7960777   .0412138    -4.41   0.000     .7192633    .8810956
        int3 |      .7358   .0810124    -2.79   0.005      .592982    .9130154
     own2008 |   .9882847   .0122926    -0.95   0.343     .9644829    1.012674
   2.variety |   .9109834   .0651515    -1.30   0.192     .7918345    1.048061
      medpur |   1.074538   .1037688     0.74   0.457     .8892437    1.298443
             |
       month |
          4  |   .9473652   .0395824    -1.29   0.196     .8728768     1.02821
          5  |   .9075163   .0532903    -1.65   0.098     .8088556    1.018211
          6  |   .8372078   .0501545    -2.97   0.003     .7444584    .9415126
          7  |   .8087461   .0532317    -3.23   0.001     .7108633    .9201071
          8  |   .8206356   .0554429    -2.93   0.003     .7188568    .9368246
          9  |    .822092   .0509416    -3.16   0.002     .7280731    .9282519
         10  |    .798263   .0556604    -3.23   0.001     .6962967    .9151614
         11  |   .8387067   .0693646    -2.13   0.033     .7132014    .9862977
             |
       _cons |    4.94532   .5007115    15.79   0.000     4.055184    6.030845
------------------------------------------------------------------------------

. 
. //Who is your source of information?
. logit sourceother int2 phone int3 own2008 i.variety medpur i.month if (varie
> ty==1 | variety==2) & uvmtag==1, or clus(mzid)

Iteration 0:   log pseudolikelihood = -5272.7228  
Iteration 1:   log pseudolikelihood = -3923.8276  
Iteration 2:   log pseudolikelihood =  -3692.594  
Iteration 3:   log pseudolikelihood = -3680.5844  
Iteration 4:   log pseudolikelihood =  -3680.372  
Iteration 5:   log pseudolikelihood = -3680.3717  

Logistic regression                               Number of obs   =      10267
                                                  Wald chi2(14)   =     243.54
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -3680.3717                 Pseudo R2       =     0.3020

                                  (Std. Err. adjusted for 72 clusters in mzid)
------------------------------------------------------------------------------
             |               Robust
 sourceother | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        int2 |   3.530272   2.085476     2.14   0.033     1.109095    11.23693
       phone |   11.16144   5.987114     4.50   0.000     3.900561    31.93844
        int3 |   52.17259   33.08292     6.24   0.000     15.05535    180.7981
     own2008 |   .9321594   .0709843    -0.92   0.356     .8029173    1.082205
   2.variety |   2.959907   .7935044     4.05   0.000     1.750187    5.005781
      medpur |   4.823454   3.690123     2.06   0.040     1.076855    21.60524
             |
       month |
          4  |   1.398624   .4723935     0.99   0.321     .7214441    2.711436
          5  |   1.136253   .4150463     0.35   0.727     .5553305    2.324871
          6  |   1.008869   .3476827     0.03   0.980     .5134387    1.982354
          7  |   .9014613   .3441882    -0.27   0.786     .4265291    1.905222
          8  |   .9454041   .3592811    -0.15   0.883      .448886    1.991127
          9  |    .929626   .3715543    -0.18   0.855     .4247161    2.034781
         10  |   .8046827   .3493861    -0.50   0.617       .34359    1.884555
         11  |   .7366989    .303515    -0.74   0.458     .3285495    1.651883
             |
       _cons |   .0045833   .0043865    -5.63   0.000     .0007023    .0299107
------------------------------------------------------------------------------

. 
. *****Panel B: Change in normalized error
. //Normalized error in tracked price = (tracked price - mandi price)/mandi pr
> ice. Test if error is lower for intervention farmers.
. gen e=trackedpx-mandipx
(2249 missing values generated)

. gen norme=e/mandipx
(2249 missing values generated)

. gen normesq=norme^2
(2249 missing values generated)

. 
. //Change in normalized error
. tabstat normesq if (variety==1|variety==2) & uvmtag==1, stats(mean N) by(nop
> honecontrol) save

Summary for variables: normesq
     by categories of: nophonecontrol 

nophonecontrol |      mean         N
---------------+--------------------
             0 |  .2213675      3046
             1 |  .1904478      2588
---------------+--------------------
         Total |  .2071644      5634
------------------------------------

. matrix S1=r(Stat1)

. matrix S2=r(Stat2)

. di S1[1,1]
.22136752

. di S2[1,1]
.19044776

. di S1[2,1]
3046

. di S2[2,1]
2588

. local f=S1[1,1]/S2[1,1]

. di 1-F(S1[2,1]-1,S2[2,1]-1,`f')
.00003635

. 
. //With phone v. control
. tabstat normesq if (variety==1|variety==2) & uvmtag==1, stats(mean N) by(wit
> hphonecontrol) save

Summary for variables: normesq
     by categories of: withphonecontrol 

withphonecontrol |      mean         N
-----------------+--------------------
               0 |  .2213675      3046
               1 |  .1786832       688
-----------------+--------------------
           Total |  .2135028      3734
--------------------------------------

. matrix S1=r(Stat1)

. matrix S2=r(Stat2)

. di S1[1,1]
.22136752

. di S2[1,1]
.17868319

. di S1[2,1]
3046

. di S2[2,1]
688

. local f=S1[1,1]/S2[1,1]

. di 1-F(S1[2,1]-1,S2[2,1]-1,`f')
.00023975

. 
. //With phone v. without phone
. tabstat normesq if (variety==1|variety==2) & uvmtag==1, stats(mean N) by(wit
> hphonenophone) save

Summary for variables: normesq
     by categories of: withphonenophone 

withphonenophone |      mean         N
-----------------+--------------------
               0 |  .1904478      2588
               1 |  .1786832       688
-----------------+--------------------
           Total |  .1879771      3276
--------------------------------------

. matrix S1=r(Stat1)

. matrix S2=r(Stat2)

. di S1[1,1]
.19044776

. di S2[1,1]
.17868319

. di S1[2,1]
2588

. di S2[2,1]
688

. local f=S1[1,1]/S2[1,1]

. di 1-F(S1[2,1]-1,S2[2,1]-1,`f')
.15102137

. 
. //Public information v. control
. tabstat normesq if (variety==1|variety==2) & uvmtag==1, stats(mean N) by(vil
> lagecontrol) save

Summary for variables: normesq
     by categories of: villagecontrol 

villagecontrol |      mean         N
---------------+--------------------
             0 |  .2213675      3046
             1 |  .1807948      4714
---------------+--------------------
         Total |  .1967207      7760
------------------------------------

. matrix S1=r(Stat1)

. matrix S2=r(Stat2)

. di S1[1,1]
.22136752

. di S2[1,1]
.18079482

. di S1[2,1]
3046

. di S2[2,1]
4714

. local f=S1[1,1]/S2[1,1]

. di 1-F(S1[2,1]-1,S2[2,1]-1,`f')
2.838e-10

. 
. //Public information v. private information
. tabstat normesq if (variety==1|variety==2) & uvmtag==1, stats(mean N) by(vil
> lagemobile) save

Summary for variables: normesq
     by categories of: villagemobile 

villagemobile |      mean         N
--------------+--------------------
            0 |  .1879771      3276
            1 |  .1807948      4714
--------------+--------------------
        Total |  .1837396      7990
-----------------------------------

. matrix S1=r(Stat1)

. matrix S2=r(Stat2)

. di S1[1,1]
.18797706

. di S2[1,1]
.18079482

. di S1[2,1]
3276

. di S2[2,1]
4714

. local f=S1[1,1]/S2[1,1]

. di 1-F(S1[2,1]-1,S2[2,1]-1,`f')
.1123491

. 
. *******************Table 5: Average Effects
. use analysisdata, clear

. 
. //Without mandi fixed effects: Quantity sold
. reg Totsold int2 phone int3 own2008 i.variety i.quality medpur if (variety==
> 1 | variety==2) & uvqtag==1 & Netpricercvd~=., clus(mzid)

Linear regression                                      Number of obs =    2318
                                                       F(  7,    71) =   37.53
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.3533
                                                       Root MSE      =  5012.4

                                  (Std. Err. adjusted for 72 clusters in mzid)
------------------------------------------------------------------------------
             |               Robust
     Totsold |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        int2 |   457.6351   552.9215     0.83   0.411     -644.859    1560.129
       phone |   639.8922   417.8301     1.53   0.130    -193.2373    1473.022
        int3 |    230.537   522.0779     0.44   0.660    -810.4567    1271.531
     own2008 |   2251.884   174.7728    12.88   0.000     1903.397    2600.371
   2.variety |  -2433.209   481.6905    -5.05   0.000    -3393.673   -1472.745
   2.quality |  -5339.434   395.9129   -13.49   0.000    -6128.862   -4550.006
      medpur |   281.9989   421.6345     0.67   0.506    -558.7165    1122.714
       _cons |   2817.063   551.6628     5.11   0.000     1717.078    3917.047
------------------------------------------------------------------------------

. summ Totsold if (int2==0 & int3==0) & (variety==1|variety==2) & uvqtag==1 & 
> Netpricercvd~=. 

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
     Totsold |       724    3855.331    5738.745         10      60500

. global SE_DV = r(sd)/sqrt(r(N))

. di $SE_DV
213.27877

. 
. //Without mandi fixed effects: Net price received
. reg Netdiscprice int2 phone int3 own2008 i.variety i.quality medpur if (vari
> ety==1 | variety==2) & uvqtag==1 & Netpricercvd~=., clus(mzid)

Linear regression                                      Number of obs =    2318
                                                       F(  7,    71) =   29.01
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.3318
                                                       Root MSE      =  .74469

                                  (Std. Err. adjusted for 72 clusters in mzid)
------------------------------------------------------------------------------
             |               Robust
Netdiscprice |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        int2 |  -.0778286   .1282018    -0.61   0.546    -.3334558    .1777985
       phone |   .0907121   .0959273     0.95   0.348    -.1005616    .2819858
        int3 |  -.1007209     .12032    -0.84   0.405    -.3406323    .1391904
     own2008 |  -.1016036   .0183177    -5.55   0.000    -.1381281   -.0650791
   2.variety |   .9700134   .1194765     8.12   0.000     .7317841    1.208243
   2.quality |   -.865841   .1014711    -8.53   0.000    -1.068169   -.6635135
      medpur |   .2378739   .0918799     2.59   0.012     .0546707    .4210772
       _cons |   2.168728   .1194415    18.16   0.000     1.930568    2.406887
------------------------------------------------------------------------------

. summ Netdiscprice if (int2==0 & int3==0) & (variety==1|variety==2) & uvqtag=
> =1 & Netpricercvd~=. 

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
Netdiscprice |       724    2.020556    .8749654  -.0891622   7.416252

. global SE_DV = r(sd)/sqrt(r(N))

. di $SE_DV
.03251783

. 
. //With mandi fixed effects: Quantity sold
. areg Totsold int2 phone int3 own2008 i.variety i.quality if (variety==1 | va
> riety==2) & uvqtag==1 & Netpricercvd~=., absorb(mv_mktgroup) clus(mzid)
note: 2.variety omitted because of collinearity

Linear regression, absorbing indicators           Number of obs   =       2318
                                                  F(   5,     71) =      43.47
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.3874
                                                  Adj R-squared   =     0.3802
                                                  Root MSE        =  4899.5354

                                  (Std. Err. adjusted for 72 clusters in mzid)
------------------------------------------------------------------------------
             |               Robust
     Totsold |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        int2 |  -30.71047    531.372    -0.06   0.954    -1090.236    1028.815
       phone |   567.2784   433.7482     1.31   0.195     -297.591    1432.148
        int3 |  -289.7549   512.6597    -0.57   0.574    -1311.969    732.4596
     own2008 |   2215.653   178.3922    12.42   0.000     1859.949    2571.357
   2.variety |          0  (omitted)
   2.quality |  -5267.049   438.9173   -12.00   0.000    -6142.225   -4391.873
       _cons |   3034.078   452.4165     6.71   0.000     2131.985    3936.171
-------------+----------------------------------------------------------------
 mv_mktgroup |   absorbed                                      (23 categories)

. summ Totsold if (int2==0 & int3==0) & (variety==1|variety==2) & uvqtag==1 & 
> Netpricercvd~=. 

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
     Totsold |       724    3855.331    5738.745         10      60500

. global SE_DV = r(sd)/sqrt(r(N))

. di $SE_DV
213.27877

. 
. //With mandi fixed effects: Net price received
. areg Netdiscprice int2 phone int3 own2008 i.variety i.quality if (variety==1
>  | variety==2) & uvqtag==1 & Netpricercvd~=., absorb(mv_mktgroup) clus(mzid)
note: 2.variety omitted because of collinearity

Linear regression, absorbing indicators           Number of obs   =       2318
                                                  F(   5,     71) =      23.69
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.3999
                                                  Adj R-squared   =     0.3928
                                                  Root MSE        =     0.7088

                                  (Std. Err. adjusted for 72 clusters in mzid)
------------------------------------------------------------------------------
             |               Robust
Netdiscprice |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        int2 |   .0159563   .1148079     0.14   0.890    -.2129643    .2448768
       phone |   .0803045   .0890772     0.90   0.370    -.0973104    .2579194
        int3 |  -.0519131   .1100064    -0.47   0.638    -.2712597    .1674336
     own2008 |    -.08279   .0154396    -5.36   0.000    -.1135756   -.0520043
   2.variety |          0  (omitted)
   2.quality |  -.8595232   .0998694    -8.61   0.000    -1.058657   -.6603893
       _cons |   2.327183   .0893626    26.04   0.000     2.148999    2.505367
-------------+----------------------------------------------------------------
 mv_mktgroup |   absorbed                                      (23 categories)

. summ Netdiscprice if (int2==0 & int3==0) & (variety==1|variety==2) & uvqtag=
> =1 & Netpricercvd~=. 

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
Netdiscprice |       724    2.020556    .8749654  -.0891622   7.416252

. global SE_DV = r(sd)/sqrt(r(N))

. di $SE_DV
.03251783

. 
. 
. *******************Tables 6 & 7: Heterogenous Effects on Quantity Sold & Net
>  Price Received. Also Appendix Table A4.
. use analysisdata, clear

. 
. foreach v in Totsold Netdiscprice {
  2.  //Column 1: Farmer-specific average of mandi price
.  areg `v' Avgmktpr int2 int2avgmktpr phone phoneavgmktpr int3 int3avgmktpr o
> wn2008 i.variety i.quality if (variety==1 | variety==2) & uvqtag==1 & Netpri
> cercvd~=., absorb(mv_mktgroup) clus(mzid)
  3.  summ `v' if (int2==0 & int3==0) & (variety==1|variety==2) & uvqtag==1 & 
> Netpricercvd~=. & Avgmktpr~=.
  4.  global SE_DV = r(sd)/sqrt(r(N))
  5.  di $SE_DV
  6. 
.  //Column 2: Weighted average of mandi price (using district weights)
.  areg `v' Avgmktpr_district_08 int2 int2avgmktpr_district_08 phone phoneavgm
> ktpr_district_08 int3 int3avgmktpr_district_08 own2008 i.variety i.quality i
> f (variety==1 | variety==2) & uvqtag==1 & Netpricercvd~=., absorb(mv_mktgrou
> p) clus(mzid)
  7.  summ `v' if (int2==0 & int3==0) & (variety==1|variety==2) & uvqtag==1 & 
> Netpricercvd~=. & Avgmktpr_district_08~=.
  8.  global SE_DV = r(sd)/sqrt(r(N))
  9.  di $SE_DV
 10. 
.  //Column 3: Farmer-specific average deviation of mandi price from expected 
> price
.  reg `v' Dev_mandipx int2 int2devmandipx phone phonedevmandipx int3 int3devm
> andipx own2008 medpur i.variety i.quality if (variety==1 | variety==2) & uvq
> tag==1 & Netpricercvd~=., vce(boot, cluster(mzid) reps(400) seed(2906))
 11.  summ `v' if (int2==0 & int3==0) & Dev_mandipx~=.
 12.  global SE_DV = r(sd)/sqrt(r(N))
 13.  di $SE_DV
 14. 
.  //Column 4: Farmer-specific instrumented mandi price
.  ivregress2 2sls `v' int2 phone int3 (Avgmktpr int2avgmktpr phoneavgmktpr in
> t3avgmktpr = dxretail int2dxretail phonedxretail int3dxretail) own2008 i.var
> iety i.quality i.mv_mktgroup if (variety==1 | variety==2) & uvqtag==1 & Netp
> ricercvd~=., clus(mzid)
 15.  summ `v' if (int2==0 & int3==0) & (variety==1|variety==2) & uvqtag==1 & 
> Netpricercvd~=. & Avgmktpr~=.
 16.  global SE_DV = r(sd)/sqrt(r(N))
 17.  di $SE_DV
 18. 
.  //Column 5: Farmers with long-term relationships
.  areg `v' Avgmktpr int2 phone int3 int2avgmktpr phoneavgmktpr int3avgmktpr o
> wn2008 i.quality if sellertolongbuyer==1 & (variety==1 | variety==2) & uvqta
> g==1 & Netpricercvd~=., absorb(mv_mktgroup) clus(mzid)
 19.  summ `v' if (int2==0 & int3==0) & (variety==1|variety==2) & uvqtag==1 & 
> Netpricercvd~=. & Avgmktpr~=. & sellertolongbuyer==1
 20.  global SE_DV = r(sd)/sqrt(r(N))
 21.  di $SE_DV
 22. 
.  //Columns 1 & 2 in Table A4: Households not asked about price tracking
.  areg `v' Avgmktpr int2 phone int3 int2avgmktpr phoneavgmktpr int3avgmktpr o
> wn2008 i.quality if askedi2==0 & (variety==1 | variety==2) & uvqtag==1 & Net
> pricercvd~=., absorb(mv_mktgroup) clus(mzid)
 23.  summ `v' if (int2==0 & int3==0) & (variety==1|variety==2) & uvqtag==1 & 
> Netpricercvd~=. & Avgmktpr~=. & askedi2==0
 24.  global SE_DV = r(sd)/sqrt(r(N))
 25.  di $SE_DV
 26. }
note: 2.variety omitted because of collinearity

Linear regression, absorbing indicators           Number of obs   =       2300
                                                  F(   9,     71) =      25.13
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.3924
                                                  Adj R-squared   =     0.3844
                                                  Root MSE        =  4897.8890

                                  (Std. Err. adjusted for 72 clusters in mzid)
------------------------------------------------------------------------------
             |               Robust
     Totsold |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    Avgmktpr |   76.57886   242.7646     0.32   0.753    -407.4801    560.6378
        int2 |  -3155.493   1358.708    -2.32   0.023     -5864.68   -446.3067
int2avgmktpr |   708.1945   320.5215     2.21   0.030     69.09277    1347.296
       phone |   1418.294   1419.841     1.00   0.321    -1412.789    4249.377
phoneavgmk~r |  -200.9483   332.0894    -0.61   0.547    -863.1159    461.2192
        int3 |  -2946.121   1263.437    -2.33   0.023    -5465.344   -426.8991
int3avgmktpr |   602.3688   287.9261     2.09   0.040     28.26039    1176.477
     own2008 |   2186.781   181.7426    12.03   0.000     1824.397    2549.166
   2.variety |          0  (omitted)
   2.quality |  -5231.042   435.1747   -12.02   0.000    -6098.756   -4363.329
       _cons |   2794.027   1078.838     2.59   0.012     642.8848    4945.169
-------------+----------------------------------------------------------------
 mv_mktgroup |   absorbed                                      (22 categories)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
     Totsold |       718    3872.437    5758.028         10      60500
214.8877
note: Avgmktpr_district_08 omitted because of collinearity
note: 2.variety omitted because of collinearity

Linear regression, absorbing indicators           Number of obs   =       2317
                                                  F(   8,     71) =      27.61
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.3902
                                                  Adj R-squared   =     0.3824
                                                  Root MSE        =  4891.5301

                                  (Std. Err. adjusted for 72 clusters in mzid)
------------------------------------------------------------------------------
             |               Robust
     Totsold |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
Avgmktpr_~08 |          0  (omitted)
        int2 |  -3910.547   1774.327    -2.20   0.031    -7448.455   -372.6387
int2avgmk~08 |   913.9244   429.2978     2.13   0.037     57.92886     1769.92
       phone |  -66.76542   1578.911    -0.04   0.966    -3215.024    3081.493
phoneavgm~08 |   144.9635    411.184     0.35   0.725    -674.9141     964.841
        int3 |  -3173.792   1776.177    -1.79   0.078    -6715.388    367.8038
int3avgmk~08 |   663.4543   413.1651     1.61   0.113    -160.3735    1487.282
     own2008 |   2198.242   178.2288    12.33   0.000     1842.864    2553.621
   2.variety |          0  (omitted)
   2.quality |  -5229.923   440.3388   -11.88   0.000    -6107.934   -4351.913
       _cons |   3084.029   422.9646     7.29   0.000     2240.661    3927.396
-------------+----------------------------------------------------------------
 mv_mktgroup |   absorbed                                      (22 categories)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
     Totsold |       723    3858.797     5741.96         10      60500
213.54577
(running regress on estimation sample)

Bootstrap replications (400)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5 
..................................................    50
..................................................   100
..................................................   150
..................................................   200
..................................................   250
..................................................   300
..................................................   350
..................................................   400

Linear regression                               Number of obs      =      2283
                                                Replications       =       400
                                                Wald chi2(11)      =    277.58
                                                Prob > chi2        =    0.0000
                                                R-squared          =    0.3621
                                                Adj R-squared      =    0.3590
                                                Root MSE           = 4958.4013

                                   (Replications based on 72 clusters in mzid)
------------------------------------------------------------------------------
             |   Observed   Bootstrap                         Normal-based
     Totsold |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 Dev_mandipx |  -252.1842   93.62695    -2.69   0.007    -435.6896   -68.67871
        int2 |   562.4579   676.2579     0.83   0.406    -762.9832    1887.899
int2devman~x |   827.5732   344.8502     2.40   0.016     151.6792    1503.467
       phone |   621.7913   664.5849     0.94   0.349    -680.7712    1924.354
phonedevma~x |  -68.94517   337.9999    -0.20   0.838    -731.4128    593.5225
        int3 |  -140.0976   541.6608    -0.26   0.796    -1201.733     921.538
int3devman~x |   145.1789   200.5592     0.72   0.469      -247.91    538.2678
     own2008 |   2253.314   162.2646    13.89   0.000     1935.281    2571.346
      medpur |   174.0632   479.4807     0.36   0.717    -765.7017    1113.828
   2.variety |  -2261.326   521.2497    -4.34   0.000    -3282.956   -1239.695
   2.quality |  -5330.825   397.3395   -13.42   0.000    -6109.596   -4552.054
       _cons |   3158.279   558.0335     5.66   0.000     2064.553    4252.004
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
     Totsold |      1582    5018.742    6876.709         10      60500
172.893
note: 4.mv_mktgroup identifies no observations in the sample
note: 6.mv_mktgroup identifies no observations in the sample
note: 10.mv_mktgroup identifies no observations in the sample
note: 17.mv_mktgroup identifies no observations in the sample
note: 18.mv_mktgroup identifies no observations in the sample
note: 21.mv_mktgroup identifies no observations in the sample
note: 22.mv_mktgroup identifies no observations in the sample
note: 25.mv_mktgroup identifies no observations in the sample
note: 32.mv_mktgroup identifies no observations in the sample
note: 33.mv_mktgroup identifies no observations in the sample
note: 39.mv_mktgroup identifies no observations in the sample
note: 41.mv_mktgroup identifies no observations in the sample
note: 42.mv_mktgroup identifies no observations in the sample
note: 50.mv_mktgroup identifies no observations in the sample
note: 53.mv_mktgroup omitted because of collinearity

Instrumental variables (2SLS) regression               Number of obs =    1508
                                                       Wald chi2(23) = 1104.40
                                                       Prob > chi2   =  0.0000
                                                       R-squared     =  0.4473
                                                       Root MSE      =  4634.8

                                  (Std. Err. adjusted for 62 clusters in mzid)
------------------------------------------------------------------------------
             |               Robust
     Totsold |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    Avgmktpr |    205.612   657.7498     0.31   0.755    -1083.554    1494.778
int2avgmktpr |   932.2586   534.7227     1.74   0.081    -115.7787    1980.296
phoneavgmk~r |   855.6651   1021.243     0.84   0.402    -1145.934    2857.265
int3avgmktpr |   829.0915   649.9159     1.28   0.202    -444.7202    2102.903
        int2 |  -4109.401   2303.943    -1.78   0.074    -8625.046    406.2453
       phone |  -2048.766   3706.145    -0.55   0.580    -9312.677    5215.145
        int3 |  -4153.124   2741.344    -1.51   0.130     -9526.06    1219.812
     own2008 |   2601.403   236.8596    10.98   0.000     2137.167     3065.64
   2.variety |  -6511.086   1458.766    -4.46   0.000    -9370.216   -3651.957
   2.quality |  -4656.223   568.0043    -8.20   0.000    -5769.491   -3542.955
             |
 mv_mktgroup |
          4  |          0  (empty)
          5  |  -4864.074   1408.575    -3.45   0.001     -7624.83   -2103.317
          6  |          0  (empty)
          9  |  -614.7324   1589.525    -0.39   0.699    -3730.143    2500.678
         10  |          0  (empty)
         14  |  -2056.196   1779.619    -1.16   0.248    -5544.184    1431.792
         15  |    3375.18    1421.18     2.37   0.018     589.7177    6160.643
         17  |          0  (empty)
         18  |          0  (empty)
         20  |  -2400.801   1205.825    -1.99   0.046    -4764.175   -37.42661
         21  |          0  (empty)
         22  |          0  (empty)
         23  |  -1100.163    1147.21    -0.96   0.338    -3348.654    1148.328
         25  |          0  (empty)
         26  |  -2234.435   1411.636    -1.58   0.113     -5001.19    532.3198
         29  |   983.9292   1803.106     0.55   0.585    -2550.093    4517.951
         30  |  -3246.802   538.1294    -6.03   0.000    -4301.516   -2192.088
         32  |          0  (empty)
         33  |          0  (empty)
         38  |  -4594.205   1240.793    -3.70   0.000    -7026.115   -2162.296
         39  |          0  (empty)
         41  |          0  (empty)
         42  |          0  (empty)
         47  |  -1654.376   1365.542    -1.21   0.226    -4330.789    1022.037
         48  |   2113.011   798.8795     2.64   0.008     547.2356    3678.786
         50  |          0  (empty)
         52  |  -906.1784   1470.161    -0.62   0.538     -3787.64    1975.283
         53  |          0  (omitted)
             |
       _cons |   3612.901   3495.641     1.03   0.301    -3238.431    10464.23
------------------------------------------------------------------------------
Instrumented:  Avgmktpr int2avgmktpr phoneavgmktpr int3avgmktpr
Instruments:   int2 phone int3 own2008 2.variety 2.quality 5.mv_mktgroup
               9.mv_mktgroup 14.mv_mktgroup 15.mv_mktgroup 20.mv_mktgroup
               23.mv_mktgroup 26.mv_mktgroup 29.mv_mktgroup 30.mv_mktgroup
               38.mv_mktgroup 47.mv_mktgroup 48.mv_mktgroup 52.mv_mktgroup
               dxretail int2dxretail phonedxretail int3dxretail

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
     Totsold |       718    3872.437    5758.028         10      60500
214.8877

Linear regression, absorbing indicators           Number of obs   =        443
                                                  F(   9,     55) =      17.97
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5150
                                                  Adj R-squared   =     0.4810
                                                  Root MSE        =  4268.4504

                                  (Std. Err. adjusted for 56 clusters in mzid)
------------------------------------------------------------------------------
             |               Robust
     Totsold |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    Avgmktpr |  -819.2907   476.0196    -1.72   0.091    -1773.255    134.6739
        int2 |  -5838.134   3144.505    -1.86   0.069    -12139.86    463.5948
       phone |   3343.988    4040.27     0.83   0.411    -4752.894    11440.87
        int3 |  -6570.694   2435.111    -2.70   0.009    -11450.77   -1690.622
int2avgmktpr |   1429.483   815.1279     1.75   0.085      -204.07    3063.036
phoneavgmk~r |  -724.8491   1058.414    -0.68   0.496    -2845.959    1396.261
int3avgmktpr |   1599.767   563.6126     2.84   0.006     470.2624    2729.272
     own2008 |    2463.81   405.4345     6.08   0.000     1651.301    3276.319
   2.quality |   -5448.23   566.8087    -9.61   0.000     -6584.14    -4312.32
       _cons |   6241.723   2060.092     3.03   0.004     2113.206    10370.24
-------------+----------------------------------------------------------------
 mv_mktgroup |   absorbed                                      (21 categories)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
     Totsold |       104    3780.481    4457.163         50      30000
437.06084

Linear regression, absorbing indicators           Number of obs   =       1139
                                                  F(   9,     71) =      19.18
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.4049
                                                  Adj R-squared   =     0.3888
                                                  Root MSE        =  4709.7490

                                  (Std. Err. adjusted for 72 clusters in mzid)
------------------------------------------------------------------------------
             |               Robust
     Totsold |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    Avgmktpr |  -1.300147   322.3917    -0.00   0.997    -644.1309    641.5306
        int2 |  -2944.751   1678.458    -1.75   0.084      -6291.5    401.9993
       phone |   2609.029   2029.408     1.29   0.203    -1437.496    6655.554
        int3 |  -3972.866   1676.522    -2.37   0.021    -7315.757   -629.9755
int2avgmktpr |   544.4755   381.9448     1.43   0.158    -217.1007    1306.052
phoneavgmk~r |  -479.9436   445.9167    -1.08   0.285    -1369.076     409.189
int3avgmktpr |   766.8261   376.8935     2.03   0.046     15.32168     1518.33
     own2008 |   2002.404   201.1541     9.95   0.000     1601.314    2403.494
   2.quality |  -5120.195   459.5944   -11.14   0.000      -6036.6    -4203.79
       _cons |   3520.771   1408.693     2.50   0.015     711.9161    6329.626
-------------+----------------------------------------------------------------
 mv_mktgroup |   absorbed                                      (22 categories)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
     Totsold |       356    4060.323    6575.658         10      60500
348.50917
note: 2.variety omitted because of collinearity

Linear regression, absorbing indicators           Number of obs   =       2300
                                                  F(   9,     71) =      23.58
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.4228
                                                  Adj R-squared   =     0.4151
                                                  Root MSE        =     0.6918

                                  (Std. Err. adjusted for 72 clusters in mzid)
------------------------------------------------------------------------------
             |               Robust
Netdiscprice |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    Avgmktpr |   .1572376   .0708184     2.22   0.030     .0160297    .2984456
        int2 |  -.5646917    .304882    -1.85   0.068    -1.172609    .0432258
int2avgmktpr |   .1323165    .070643     1.87   0.065    -.0085417    .2731746
       phone |   .0025622   .3036558     0.01   0.993    -.6029103    .6080347
phoneavgmk~r |   .0142113   .0730175     0.19   0.846    -.1313813     .159804
        int3 |   .1298265   .2696391     0.48   0.632    -.4078186    .6674716
int3avgmktpr |  -.0360787   .0658735    -0.55   0.586    -.1674268    .0952693
     own2008 |  -.0836861   .0144056    -5.81   0.000    -.1124101   -.0549621
   2.variety |          0  (omitted)
   2.quality |   -.841267   .0989171    -8.50   0.000    -1.038502    -.644032
       _cons |   1.626751    .303592     5.36   0.000     1.021405    2.232096
-------------+----------------------------------------------------------------
 mv_mktgroup |   absorbed                                      (22 categories)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
Netdiscprice |       718    2.014682    .8708607  -.0891622   7.416252
.03250023
note: Avgmktpr_district_08 omitted because of collinearity
note: 2.variety omitted because of collinearity

Linear regression, absorbing indicators           Number of obs   =       2317
                                                  F(   8,     71) =      19.01
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.4057
                                                  Adj R-squared   =     0.3981
                                                  Root MSE        =     0.7051

                                  (Std. Err. adjusted for 72 clusters in mzid)
------------------------------------------------------------------------------
             |               Robust
Netdiscprice |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
Avgmktpr_~08 |          0  (omitted)
        int2 |  -.6852093   .3823234    -1.79   0.077    -1.447541    .0771219
int2avgmk~08 |   .1669825   .0935478     1.78   0.079    -.0195464    .3535115
       phone |   .0425819   .2540285     0.17   0.867    -.4639365    .5491002
phoneavgm~08 |    .007899   .0657473     0.12   0.905    -.1231973    .1389953
        int3 |  -.1211673   .3607307    -0.34   0.738    -.8404439    .5981093
int3avgmk~08 |   .0173291   .0884731     0.20   0.845    -.1590812    .1937394
     own2008 |  -.0839782    .015424    -5.44   0.000    -.1147329   -.0532235
   2.variety |          0  (omitted)
   2.quality |  -.8500096   .0994123    -8.55   0.000    -1.048232   -.6517871
       _cons |   2.326462   .0855527    27.19   0.000     2.155875     2.49705
-------------+----------------------------------------------------------------
 mv_mktgroup |   absorbed                                      (22 categories)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
Netdiscprice |       723    2.017909    .8726655  -.0891622   7.416252
.03245478
(running regress on estimation sample)

Bootstrap replications (400)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5 
..................................................    50
..................................................   100
..................................................   150
..................................................   200
..................................................   250
..................................................   300
..................................................   350
..................................................   400

Linear regression                               Number of obs      =      2283
                                                Replications       =       400
                                                Wald chi2(11)      =    266.10
                                                Prob > chi2        =    0.0000
                                                R-squared          =    0.3556
                                                Adj R-squared      =    0.3525
                                                Root MSE           =    0.7295

                                   (Replications based on 72 clusters in mzid)
------------------------------------------------------------------------------
             |   Observed   Bootstrap                         Normal-based
Netdiscprice |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 Dev_mandipx |   .0341513   .0221332     1.54   0.123    -.0092291    .0775317
        int2 |   .0611572   .1215131     0.50   0.615    -.1770042    .2993185
int2devman~x |   .1574472   .0555132     2.84   0.005     .0486433    .2662511
       phone |   .1597029   .1101693     1.45   0.147     -.056225    .3756307
phonedevma~x |   .1079986   .0913299     1.18   0.237    -.0710047    .2870019
        int3 |  -.1031552   .1239659    -0.83   0.405    -.3461239    .1398135
int3devman~x |  -.0348192    .045422    -0.77   0.443    -.1238447    .0542062
     own2008 |  -.0927931    .017945    -5.17   0.000    -.1279647   -.0576216
      medpur |    .072445   .1077954     0.67   0.502    -.1388301    .2837201
   2.variety |   1.080951   .1206534     8.96   0.000     .8444745    1.317427
   2.quality |  -.8457747   .0998459    -8.47   0.000    -1.041469   -.6500804
       _cons |   2.211183   .1306816    16.92   0.000     1.955052    2.467314
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
Netdiscprice |      1582    2.151421    .8076669  -.0891622   7.416252
.02030622
note: 4.mv_mktgroup identifies no observations in the sample
note: 6.mv_mktgroup identifies no observations in the sample
note: 10.mv_mktgroup identifies no observations in the sample
note: 17.mv_mktgroup identifies no observations in the sample
note: 18.mv_mktgroup identifies no observations in the sample
note: 21.mv_mktgroup identifies no observations in the sample
note: 22.mv_mktgroup identifies no observations in the sample
note: 25.mv_mktgroup identifies no observations in the sample
note: 32.mv_mktgroup identifies no observations in the sample
note: 33.mv_mktgroup identifies no observations in the sample
note: 39.mv_mktgroup identifies no observations in the sample
note: 41.mv_mktgroup identifies no observations in the sample
note: 42.mv_mktgroup identifies no observations in the sample
note: 50.mv_mktgroup identifies no observations in the sample
note: 53.mv_mktgroup omitted because of collinearity

Instrumental variables (2SLS) regression               Number of obs =    1508
                                                       Wald chi2(23) = 4527.34
                                                       Prob > chi2   =  0.0000
                                                       R-squared     =  0.3387
                                                       Root MSE      =  .67071

                                  (Std. Err. adjusted for 62 clusters in mzid)
------------------------------------------------------------------------------
             |               Robust
Netdiscprice |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    Avgmktpr |   .4928414   .1656282     2.98   0.003     .1682161    .8174667
int2avgmktpr |   .1109415   .0887007     1.25   0.211    -.0629086    .2847916
phoneavgmk~r |  -.0607198   .0788023    -0.77   0.441    -.2151696    .0937299
int3avgmktpr |  -.0980964    .099008    -0.99   0.322    -.2921484    .0959556
        int2 |  -.4921363   .3964445    -1.24   0.214    -1.269153    .2848807
       phone |   .3075528   .3354679     0.92   0.359    -.3499523    .9650578
        int3 |    .491898   .4297203     1.14   0.252    -.3503384    1.334134
     own2008 |  -.0796121   .0186339    -4.27   0.000    -.1161338   -.0430903
   2.variety |   .0876519   .2101685     0.42   0.677    -.3242707    .4995745
   2.quality |  -.7608722   .1385492    -5.49   0.000    -1.032424   -.4893208
             |
 mv_mktgroup |
          4  |          0  (empty)
          5  |  -.2642879   .1937142    -1.36   0.172    -.6439607    .1153848
          6  |          0  (empty)
          9  |   1.174403   .4071159     2.88   0.004     .3764711    1.972336
         10  |          0  (empty)
         14  |    1.02623   .3913405     2.62   0.009     .2592163    1.793243
         15  |   1.151734   .4593076     2.51   0.012     .2515072     2.05196
         17  |          0  (empty)
         18  |          0  (empty)
         20  |   .1806804   .1859036     0.97   0.331     -.183684    .5450449
         21  |          0  (empty)
         22  |          0  (empty)
         23  |   .0498491   .1362544     0.37   0.714    -.2172046    .3169028
         25  |          0  (empty)
         26  |  -.1298757   .1550502    -0.84   0.402    -.4337686    .1740171
         29  |   .6983075   .4391556     1.59   0.112    -.1624217    1.559037
         30  |  -.2022263   .1306883    -1.55   0.122    -.4583707    .0539182
         32  |          0  (empty)
         33  |          0  (empty)
         38  |   .2837696   .1531331     1.85   0.064    -.0163657    .5839048
         39  |          0  (empty)
         41  |          0  (empty)
         42  |          0  (empty)
         47  |   1.053199   .2893629     3.64   0.000     .4860582     1.62034
         48  |   1.684945   .1935572     8.71   0.000     1.305579     2.06431
         50  |          0  (empty)
         52  |   .8052589   .3546042     2.27   0.023     .1102474     1.50027
         53  |          0  (omitted)
             |
       _cons |  -.4355516   .8775019    -0.50   0.620    -2.155424    1.284321
------------------------------------------------------------------------------
Instrumented:  Avgmktpr int2avgmktpr phoneavgmktpr int3avgmktpr
Instruments:   int2 phone int3 own2008 2.variety 2.quality 5.mv_mktgroup
               9.mv_mktgroup 14.mv_mktgroup 15.mv_mktgroup 20.mv_mktgroup
               23.mv_mktgroup 26.mv_mktgroup 29.mv_mktgroup 30.mv_mktgroup
               38.mv_mktgroup 47.mv_mktgroup 48.mv_mktgroup 52.mv_mktgroup
               dxretail int2dxretail phonedxretail int3dxretail

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
Netdiscprice |       718    2.014682    .8708607  -.0891622   7.416252
.03250023

Linear regression, absorbing indicators           Number of obs   =        443
                                                  F(   9,     55) =      14.68
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5126
                                                  Adj R-squared   =     0.4783
                                                  Root MSE        =     0.7362

                                  (Std. Err. adjusted for 56 clusters in mzid)
------------------------------------------------------------------------------
             |               Robust
Netdiscprice |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    Avgmktpr |   .2224625   .2176946     1.02   0.311    -.2138072    .6587322
        int2 |   .3860633   .7178624     0.54   0.593    -1.052565    1.824692
       phone |   1.095376   .7856294     1.39   0.169    -.4790601    2.669813
        int3 |   .7119843   .7112884     1.00   0.321    -.7134695    2.137438
int2avgmktpr |  -.0767019   .1719174    -0.45   0.657    -.4212321    .2678282
phoneavgmk~r |  -.2855233   .2219334    -1.29   0.204    -.7302878    .1592413
int3avgmktpr |   -.169099   .1646225    -1.03   0.309      -.49901    .1608119
     own2008 |  -.0852434    .038812    -2.20   0.032    -.1630244   -.0074624
   2.quality |  -1.113681   .1520627    -7.32   0.000    -1.418422   -.8089406
       _cons |   1.400901   .9302383     1.51   0.138    -.4633387     3.26514
-------------+----------------------------------------------------------------
 mv_mktgroup |   absorbed                                      (21 categories)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
Netdiscprice |       104      2.1306    1.134076          0   7.416252
.11120535

Linear regression, absorbing indicators           Number of obs   =       1139
                                                  F(   9,     71) =      21.22
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.4371
                                                  Adj R-squared   =     0.4219
                                                  Root MSE        =     0.6895

                                  (Std. Err. adjusted for 72 clusters in mzid)
------------------------------------------------------------------------------
             |               Robust
Netdiscprice |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    Avgmktpr |   .1886412   .0735707     2.56   0.012     .0419454     .335337
        int2 |  -.4189447   .3183311    -1.32   0.192    -1.053679    .2157895
       phone |  -.0621279   .4340964    -0.14   0.887    -.9276916    .8034359
        int3 |   .4173121   .3297681     1.27   0.210    -.2402267    1.074851
int2avgmktpr |   .1185186   .0724258     1.64   0.106    -.0258943    .2629315
phoneavgmk~r |   .0165773   .0989142     0.17   0.867    -.1806519    .2138065
int3avgmktpr |  -.0906892   .0771295    -1.18   0.244    -.2444811    .0631027
     own2008 |  -.0815398   .0194352    -4.20   0.000    -.1202925   -.0427871
   2.quality |  -.8247182   .1003443    -8.22   0.000    -1.024799   -.6246373
       _cons |   1.445747   .3236099     4.47   0.000     .8004876    2.091007
-------------+----------------------------------------------------------------
 mv_mktgroup |   absorbed                                      (22 categories)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
Netdiscprice |       356    1.990926     .839934          0        4.6
.04451641

. 
.  //Column 6 in Table 6: Fraction sold at harvest time
.  areg c_pctqty_harvsold Avgmktpr_h int2 int2avgmktpr_h phone phoneavgmktpr_h
>  int3 int3avgmktpr_h own2008 i.variety i.quality if (variety==1 | variety==2
> ) & uvqtag==1 & Netpricercvd~=., absorb(mv_mktgroup) clus(mzid)
note: 2.variety omitted because of collinearity

Linear regression, absorbing indicators           Number of obs   =       2291
                                                  F(   9,     71) =      28.85
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.3585
                                                  Adj R-squared   =     0.3499
                                                  Root MSE        =     0.2882

                                  (Std. Err. adjusted for 72 clusters in mzid)
------------------------------------------------------------------------------
             |               Robust
c_pctqty_h~d |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  Avgmktpr_h |  -.0549653   .0196091    -2.80   0.007    -.0940647   -.0158658
        int2 |  -.0263982   .1123153    -0.24   0.815    -.2503485     .197552
int2avgmkt~h |   .0047738    .024135     0.20   0.844      -.04335    .0528976
       phone |  -.1387599   .0702326    -1.98   0.052    -.2787999      .00128
phoneavgmk~h |   .0285679   .0174483     1.64   0.106     -.006223    .0633589
        int3 |  -.0345717   .0984503    -0.35   0.727     -.230876    .1617325
int3avgmkt~h |   .0011831   .0218103     0.05   0.957    -.0423053    .0446716
     own2008 |   -.031744   .0067099    -4.73   0.000    -.0451231    -.018365
   2.variety |          0  (omitted)
   2.quality |  -.3565422   .0282687   -12.61   0.000    -.4129083    -.300176
       _cons |   .7017006   .0905379     7.75   0.000     .5211731     .882228
-------------+----------------------------------------------------------------
 mv_mktgroup |   absorbed                                      (22 categories)

.  summ c_pctqty_harvsold if (int2==0 & int3==0) & (variety==1|variety==2) & u
> vqtag==1 & Netpricercvd~=.

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
c_pctqty_h~d |       724    .3235248    .3557965          0   1.558824

.  global SE_DV = r(sd)/sqrt(r(N))

.  di $SE_DV
.01322307

. 
. 
. *******************Table 8: Price Dispersion
. use analysisdata, clear

. 
. //Measures of price dispersion within the village
. egen netdiscpxsd=sd(Netdiscprice) if (variety==1 | variety==2) & uvqtag==1 &
>  Netpricercvd~=., by(mzid)
(17150 missing values generated)

. gen netdiscpxvar=netdiscpxsd^2
(17150 missing values generated)

. egen netdiscpxmax=max(Netdiscprice) if (variety==1 | variety==2) & uvqtag==1
>  & Netpricercvd~=., by(mzid)
(17150 missing values generated)

. egen netdiscpxmin=min(Netdiscprice) if (variety==1 | variety==2) & uvqtag==1
>  & Netpricercvd~=., by(mzid)
(17150 missing values generated)

. gen netdiscpxmaxmin=abs(netdiscpxmax-netdiscpxmin)
(17150 missing values generated)

. gen Totdiscprice=Discpayrcvd/Totsold
(14278 missing values generated)

. egen totdiscpxsd=sd(Totdiscprice) if (variety==1 | variety==2) & uvqtag==1 &
>  Netpricercvd~=., by(mzid)
(17150 missing values generated)

. gen totdiscpxvar=totdiscpxsd^2
(17150 missing values generated)

. egen totdiscpxmax=max(Totdiscprice) if (variety==1 | variety==2) & uvqtag==1
>  & Netpricercvd~=., by(mzid)
(17150 missing values generated)

. egen totdiscpxmin=min(Totdiscprice) if (variety==1 | variety==2) & uvqtag==1
>  & Netpricercvd~=., by(mzid)
(17150 missing values generated)

. gen totdiscpxmaxmin=abs(totdiscpxmax-totdiscpxmin)
(17150 missing values generated)

. 
. //mzidtag variable
. egen mzvtag=tag(mzid variety) if Netpricercvd~=. & (variety==1|variety==2) &
>  uvqtag==1

. 
. ***Within the village
. //Column 1: variance of gross prices
. reg totdiscpxvar int2 int3 i.variety if mzvtag==1, robust

Linear regression                                      Number of obs =     100
                                                       F(  3,    96) =    2.12
                                                       Prob > F      =  0.1028
                                                       R-squared     =  0.0681
                                                       Root MSE      =  .54796

------------------------------------------------------------------------------
             |               Robust
totdiscpxvar |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        int2 |  -.1342381   .1540251    -0.87   0.386    -.4399755    .1714993
        int3 |  -.0494137   .1610742    -0.31   0.760    -.3691434     .270316
   2.variety |   .3031436   .1337538     2.27   0.026     .0376445    .5686428
       _cons |   .6483727   .1377684     4.71   0.000     .3749045    .9218408
------------------------------------------------------------------------------

. 
. //Column 2: range of gross prices
. reg totdiscpxmaxmin int2 int3 i.variety if mzvtag==1, robust

Linear regression                                      Number of obs =     100
                                                       F(  3,    96) =    4.79
                                                       Prob > F      =  0.0037
                                                       R-squared     =  0.1091
                                                       Root MSE      =  1.0394

------------------------------------------------------------------------------
             |               Robust
totdis~axmin |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        int2 |   .1764892   .2532965     0.70   0.488    -.3263003    .6792787
        int3 |   .3059297   .2875446     1.06   0.290    -.2648418    .8767012
   2.variety |   .7324736   .2116374     3.46   0.001     .3123767    1.152571
       _cons |   2.543273   .2253487    11.29   0.000     2.095959    2.990586
------------------------------------------------------------------------------

. 
. //Column 3: variance of net prices
. reg netdiscpxvar int2 int3 i.variety if mzvtag==1, robust

Linear regression                                      Number of obs =     100
                                                       F(  3,    96) =    2.43
                                                       Prob > F      =  0.0698
                                                       R-squared     =  0.0671
                                                       Root MSE      =  .54565

------------------------------------------------------------------------------
             |               Robust
netdiscpxvar |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        int2 |  -.1131004   .1518772    -0.74   0.458    -.4145744    .1883735
        int3 |  -.0011589   .1602448    -0.01   0.994    -.3192423    .3169244
   2.variety |    .299234     .13159     2.27   0.025     .0380299    .5604381
       _cons |   .6747038   .1361256     4.96   0.000     .4044967    .9449109
------------------------------------------------------------------------------

. 
. //Column 4: range of net prices
. reg netdiscpxmaxmin int2 int3 i.variety if mzvtag==1, robust

Linear regression                                      Number of obs =     100
                                                       F(  3,    96) =    5.31
                                                       Prob > F      =  0.0020
                                                       R-squared     =  0.1136
                                                       Root MSE      =   .9997

------------------------------------------------------------------------------
             |               Robust
netdis~axmin |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        int2 |   .2388586   .2481547     0.96   0.338    -.2537247    .7314418
        int3 |    .373003   .2750015     1.36   0.178    -.1728707    .9188766
   2.variety |   .6882097   .2024577     3.40   0.001     .2863344    1.090085
       _cons |   2.644889   .2174557    12.16   0.000     2.213242    3.076535
------------------------------------------------------------------------------

. 
. ***Across villages
. use villagehaatpxdata, clear

. 
. //Compute dispersion measures by village-week only for villages that face mu
> ltiple haats
. //Identify the villages that face multiple haats
. bys mzid variety weeksold: egen mktsd=sd(mkt)
(2277 missing values generated)

. keep if mktsd~=.
(2277 observations deleted)

. 
. collapse (sd) haatpxsd=haatpx (min) haatpxmin=haatpx (max) haatpxmax=haatpx 
> (mean) int2=int2 int3=int3 int1=int1, by(mzid variety weeksold)

. gen haatpxvar=haatpxsd^2 
(310 missing values generated)

. gen haatpxmaxmin=abs(haatpxmax-haatpxmin)
(165 missing values generated)

. replace haatpxvar=0 if haatpxvar==. & haatpxmaxmin==0
(145 real changes made)

. 
. //Column 5: variance of haat prices
. areg haatpxvar int2 int3 i.variety, absorb(weeksold) clus(mzid)

Linear regression, absorbing indicators           Number of obs   =        458
                                                  F(   3,     16) =       1.67
                                                  Prob > F        =     0.2137
                                                  R-squared       =     0.4805
                                                  Adj R-squared   =     0.4279
                                                  Root MSE        =     2.4773

                                  (Std. Err. adjusted for 17 clusters in mzid)
------------------------------------------------------------------------------
             |               Robust
   haatpxvar |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        int2 |   .2405199   .3855581     0.62   0.542    -.5768268    1.057867
        int3 |   1.234634   .8182593     1.51   0.151    -.4999984    2.969266
   2.variety |  -.8321896   .4007683    -2.08   0.054     -1.68178    .0174013
       _cons |   .9143612   .2664241     3.43   0.003     .3495674    1.479155
-------------+----------------------------------------------------------------
    weeksold |   absorbed                                      (40 categories)

. 
. //Column 6: range of haat prices
. areg haatpxmaxmin int2 int3 i.variety, absorb(weeksold) clus(mzid)

Linear regression, absorbing indicators           Number of obs   =        458
                                                  F(   3,     16) =       1.66
                                                  Prob > F        =     0.2149
                                                  R-squared       =     0.3366
                                                  Adj R-squared   =     0.2695
                                                  Root MSE        =     1.1092

                                  (Std. Err. adjusted for 17 clusters in mzid)
------------------------------------------------------------------------------
             |               Robust
haatpxmaxmin |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        int2 |   .0696991   .2615531     0.27   0.793    -.4847687    .6241669
        int3 |   .3511022   .3178594     1.10   0.286    -.3227297    1.024934
   2.variety |  -.4026547   .2137306    -1.88   0.078    -.8557433     .050434
       _cons |   .8543517   .1844666     4.63   0.000        .4633    1.245403
-------------+----------------------------------------------------------------
    weeksold |   absorbed                                      (40 categories)

. 
.  
. *******************Figure 1: Intervention Impacts
. use graphdata, clear

. //Average across mandis
. gen intvn=1 if int1==1
(1490 missing values generated)

. replace intvn=2 if int2==1
(317 real changes made)

. replace intvn=3 if int3==1
(280 real changes made)

. 
. collapse (mean) farmgatepx_j mandipx_j kolpx_j bhupx_j farmgatepx_c mandipx_
> c kolpx_c bhupx_c farmgatepx_b mandipx_b kolpx_b bhupx_b, by(variety intvn w
> eeksold)

. twoway (scatter farmgatepx_b weeksold if intvn==1, scheme(s2mono) sort xline
> (12 26) xlabel(1 12 26 52, valuelabel) ytitle(Market price) xtitle(Week) con
> nect(l)) (scatter farmgatepx_b weeksold if intvn==2, sort xline(12 26) conne
> ct(l)) (scatter farmgatepx_b weeksold if intvn==3, sort xline(12 26) connect
> (l)) (scatter mandipx_b weeksold, sort xline(12 26) connect(l)), title("Mand
> i prices & Farmer net prices by intervention, 2008") legend(on title(2008) r
> ows(2) label(1 "Control") label(2 "Mobile") label(3 "Village") label(4 "Mand
> i price"))

. 
. 
. *******************Appendix Table A1: Low Bounds on Average Middleman Margin
> s
. //Row 1: Price traders sold at: See above the code for Table 1
. 
. //Row 2: Price traders bought at
. use analysisdata, clear

. gen totpricercvd=totpayrcvd/qtysold
(15499 missing values generated)

. 
. tabstat totprice if harvest==1 & soldtophmlda==1 & (variety==1 | variety==2)
> , statistics (mean semean)

    variable |      mean  se(mean)
-------------+--------------------
totpricercvd |  2.217068  .0221159
----------------------------------

. tabstat totprice if harvest==0 & soldtophmlda==1 & (variety==1 | variety==2)
> , statistics (mean semean)

    variable |      mean  se(mean)
-------------+--------------------
totpricercvd |  2.106462  .0218392
----------------------------------

. 
. //Rows 4, 5 & 6: Unit costs
. gen avgtrancost=trancost/qtysold
(15499 missing values generated)

. gen avghandcost=handcost/qtysold
(15499 missing values generated)

. gen avgstorcost=storcost/qtysold
(15499 missing values generated)

. gen avgothcost=othcost/qtysold
(15499 missing values generated)

. 
. gen paidtran=(trancost~=0 & (soldtomarket==1 | soldtophmlda==1) & (variety==
> 1 | variety==2))

. gen paidhand=(handcost~=0 & (soldtomarket==1 | soldtophmlda==1) & (variety==
> 1 | variety==2))

. gen paidstor=(storcost~=0 & (soldtomarket==1 | soldtophmlda==1) & (variety==
> 1 | variety==2))

. gen paidoth=(othcost~=0 & (soldtomarket==1 | soldtophmlda==1) & (variety==1 
> | variety==2))

. 
. //Transport cost
. summ avgtrancost if (variety==1 | variety==2) & soldtomarket==1 & paidtran==
> 1 & harvest==1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
 avgtrancost |         4        .225    .0957427         .1         .3

. local adj_avgtrancost=(round(r(mean),.01)/5)*8.5

. di `adj_avgtrancost'
.391

. 
. //Handling + Other costs: Harvest time
. summ avghandcost if (variety==1 | variety==2) & soldtomarket==1 & paidhand==
> 1 & harvest==1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
 avghandcost |        20    .2086667    .0317961         .2        .34

. local avghandcost=r(mean)

. summ avgothcost if (variety==1 | variety==2) & soldtomarket==1 & paidoth==1 
> & harvest==1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
  avgothcost |        19    .1399992    .1383285   .0166667         .5

. local avgothcost=r(mean)

. di round(`avghandcost' + `avgothcost', 0.01)
.35

. 
. //Handling + Other costs: After harvest time
. summ avghandcost if (variety==1 | variety==2) & soldtomarket==1 & paidhand==
> 1 & harvest==0

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
 avghandcost |       182    .3223155    .1255622   .0333333         .7

. local avghandcost=r(mean)

. summ avgothcost if (variety==1 | variety==2) & soldtomarket==1 & paidoth==1 
> & harvest==0

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
  avgothcost |        75    .1285278    .0887074       .002         .5

. local avgothcost=r(mean)

. di round(`avghandcost' + `avgothcost', 0.01)
.45

. 
. //Storage cost 
. summ avgstorcost if soldtomarket==1 & (variety==1 | variety==2) & storcost~=
> 0 

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
 avgstorcost |       110    .9087602    .2668107        .05        1.2

. di round(r(mean),0.01)
.91

. 
. *******************Appendix Table A2:
. use descriptivesdata, clear

. 
. gen anycult=(numcult>0)

. gen maxcultyears=maxculted if maxculted<=12
(1021 missing values generated)

. replace maxcultyears=13 if maxculted==13
(9 real changes made)

. replace maxcultyears=15 if maxculted==14
(37 real changes made)

. replace maxcultyears=16 if maxculted==15
(12 real changes made)

. replace maxcultyears=17 if maxculted==16
(3 real changes made)

. replace maxcultyears=14 if maxculted==17
(2 real changes made)

. 
. estpost tabstat own2008 maxcultage maxcultyears anypotatoes anyjyoti anycmuk
> hi areapotatoes2006 harvest2006 frachome2006 fracsold2006 price2006 fracphml
> da2006 fracmkt2006 havelandline havecellphone asktrader onlytrader askmarket
>  askfriends askmedia dontsearch, by(intvn) statistics(N mean semean) columns
> (statistics)

Summary statistics: count mean semean
     for variables: own2008 maxcultage maxcultyears anypotatoes anyjyoti anycm
> ukhi areapotatoes2006 harvest2006 frachome2006 fracsold2006 price2006 fracph
> mlda2006 fracmkt2006 havelandline havecellphone asktrader onlytrader askmark
> et askfriends askmedia dontsearch
  by categories of: intvn

       intvn |  e(count)    e(mean)  e(semean) 
-------------+---------------------------------
Control      |                                 
     own2008 |       528    1.12303   .0496639 
  maxcultage |       353   49.49858    .682046 
maxcultyears |       325   6.596923    .203628 
 anypotatoes |       531   .9868173   .0049543 
    anyjyoti |       531   .9491525   .0095426 
   anycmukhi |       531   .0508475   .0095426 
areapot~2006 |       486   .8220947   .0866458 
 harvest2006 |       486   6396.551   282.6542 
frachome2006 |       486    .049257   .0028166 
fracsold2006 |       484   .8109345    .009487 
   price2006 |       489   3.879091   .0357769 
fracphm~2006 |       489   .9887526   .0046627 
 fracmkt2006 |       489    .006135   .0035348 
havelandline |       493   .2312373   .0190083 
havecellph~e |       493   .3225152   .0210738 
   asktrader |       493   .7951318   .0181959 
  onlytrader |       493   .4868154   .0225339 
   askmarket |       493    .148073   .0160124 
  askfriends |       493   .1501014   .0161025 
    askmedia |       493   .0811359   .0123098 
  dontsearch |       493   .0040568   .0028657 
-------------+---------------------------------
Mobile       |                                 
     own2008 |       539   1.078516   .0503366 
  maxcultage |       346   48.92197   .6822152 
maxcultyears |       310   7.009677   .2010249 
 anypotatoes |       541   .9981516   .0018484 
    anyjyoti |       541   .9537893   .0090344 
   anycmukhi |       541   .1109057   .0135131 
areapot~2006 |       480   .8508542   .0478171 
 harvest2006 |       480   7186.692   376.7339 
frachome2006 |       479    .040716   .0020173 
fracsold2006 |       478   .7829881   .0100532 
   price2006 |       488   3.843934   .0396127 
fracphm~2006 |       488   .9864754   .0051367 
 fracmkt2006 |       488   .0102459   .0045633 
havelandline |       466    .248927   .0200517 
havecellph~e |       466   .3218884   .0216659 
   asktrader |       466   .6566524   .0220195 
  onlytrader |       466   .3969957   .0226896 
   askmarket |       466    .195279   .0183833 
  askfriends |       466   .1545064   .0167611 
    askmedia |       466   .0600858   .0110206 
  dontsearch |       466   .0064378   .0037088 
-------------+---------------------------------
Village      |                                 
     own2008 |       503   1.143917    .058439 
  maxcultage |       335   48.04776   .7367942 
maxcultyears |       295        7.4   .1921676 
 anypotatoes |       506          1          0 
    anyjyoti |       506   .9011858   .0132792 
   anycmukhi |       506   .1264822   .0147913 
areapot~2006 |       441   1.050689    .150533 
 harvest2006 |       441   7641.395   496.8215 
frachome2006 |       441   .0481255   .0036622 
fracsold2006 |       439   .8006587   .0103462 
   price2006 |       454   4.093221   .0368264 
fracphm~2006 |       454   .9838628   .0055705 
 fracmkt2006 |       454   .0088106   .0043907 
havelandline |       438   .2648402   .0211078 
havecellph~e |       438   .3721461   .0231231 
   asktrader |       438   .6438356   .0229072 
  onlytrader |       437   .4279176   .0236955 
   askmarket |       437   .2059497    .019367 
  askfriends |       437   .0869565   .0134944 
    askmedia |       437   .0457666   .0100083 
  dontsearch |       437   .0045767   .0032325 
-------------+---------------------------------
Total        |                                 
     own2008 |      1570   1.114439   .0304538 
  maxcultage |      1034   48.83559   .4041523 
maxcultyears |       930   6.989247   .1155717 
 anypotatoes |      1578   .9949303   .0017884 
    anyjyoti |      1578   .9353612   .0061918 
   anycmukhi |      1578   .0956907   .0074076 
areapot~2006 |      1407   .9035551   .0582243 
 harvest2006 |      1407   7056.284   224.5291 
frachome2006 |      1406   .0459923   .0016571 
fracsold2006 |      1401   .7981797   .0057504 
   price2006 |      1431   3.935037   .0218193 
fracphm~2006 |      1431   .9864247   .0029532 
 fracmkt2006 |      1431   .0083857   .0024114 
havelandline |      1397   .2476736   .0115531 
havecellph~e |      1397   .3378669   .0126591 
   asktrader |      1397   .7015032   .0122473 
  onlytrader |      1396   .4383954    .013285 
   askmarket |      1396   .1819484   .0103295 
  askfriends |      1396   .1318052   .0090571 
    askmedia |      1396   .0630372   .0065069 
  dontsearch |      1396   .0050143   .0018912 

. 
. foreach v of varlist own2008 maxcultage maxcultyears anypotatoes anyjyoti an
> ycmukhi areapotatoes2006 harvest2006 frachome2006 fracspoil2006 fracsold2006
>  price2006 fracphmlda2006 fracmkt2006 havelandline havecellphone asktrader o
> nlytrader askmarket askfriends askmedia dontsearch {
  2.  matrix `v'=J(1,2,0)
  3.  qui reg `v' villagecontrol, clus(mzid)
  4.  matrix `v'[1,1]=_b[villagecontrol]
  5.  test villagecontrol
  6.  matrix `v'[1,2]=r(p)
  7. }

 ( 1)  villagecontrol = 0

       F(  1,    47) =    0.02
            Prob > F =    0.8895

 ( 1)  villagecontrol = 0

       F(  1,    47) =    1.08
            Prob > F =    0.3037

 ( 1)  villagecontrol = 0

       F(  1,    47) =    3.65
            Prob > F =    0.0623

 ( 1)  villagecontrol = 0

       F(  1,    47) =    4.14
            Prob > F =    0.0475

 ( 1)  villagecontrol = 0

       F(  1,    47) =    1.72
            Prob > F =    0.1954

 ( 1)  villagecontrol = 0

       F(  1,    47) =    2.46
            Prob > F =    0.1232

 ( 1)  villagecontrol = 0

       F(  1,    46) =    1.40
            Prob > F =    0.2432

 ( 1)  villagecontrol = 0

       F(  1,    46) =    0.64
            Prob > F =    0.4293

 ( 1)  villagecontrol = 0

       F(  1,    46) =    0.01
            Prob > F =    0.9068

 ( 1)  villagecontrol = 0

       F(  1,    46) =    0.10
            Prob > F =    0.7558

 ( 1)  villagecontrol = 0

       F(  1,    46) =    0.09
            Prob > F =    0.7635

 ( 1)  villagecontrol = 0

       F(  1,    46) =    2.43
            Prob > F =    0.1256

 ( 1)  villagecontrol = 0

       F(  1,    46) =    0.25
            Prob > F =    0.6197

 ( 1)  villagecontrol = 0

       F(  1,    46) =    0.13
            Prob > F =    0.7248

 ( 1)  villagecontrol = 0

       F(  1,    46) =    0.14
            Prob > F =    0.7066

 ( 1)  villagecontrol = 0

       F(  1,    46) =    0.34
            Prob > F =    0.5631

 ( 1)  villagecontrol = 0

       F(  1,    46) =    4.43
            Prob > F =    0.0409

 ( 1)  villagecontrol = 0

       F(  1,    46) =    0.50
            Prob > F =    0.4818

 ( 1)  villagecontrol = 0

       F(  1,    46) =    0.68
            Prob > F =    0.4149

 ( 1)  villagecontrol = 0

       F(  1,    46) =    1.61
            Prob > F =    0.2110

 ( 1)  villagecontrol = 0

       F(  1,    46) =    1.11
            Prob > F =    0.2980

 ( 1)  villagecontrol = 0

       F(  1,    46) =    0.01
            Prob > F =    0.9223

.  
. matrix allvc = own2008 \ maxcultage \ maxcultyears \ anypotatoes \ anyjyoti 
> \ anycmukhi \ areapotatoes2006 \ harvest2006 \ frachome2006 \ fracsold2006 \
>  price2006 \ fracphmlda2006 \ fracmkt2006 \ havelandline \ havecellphone \ o
> nlytrader \ asktrader \ askmarket \ askfriends \ askmedia \ dontsearch

. matrix list allvc

allvc[21,2]
            c1          c2
r1    .0208862   .88949932
r1  -1.4508224   .30371224
r1   .80307692   .06228858
r1   .01318267   .04745749
r1  -.04796677   .19542956
r1   .07563476   .12319784
r1   .22859469   .24320318
r1   1244.8431   .42928166
r1  -.00113146   .90677164
r1  -.01027585   .76351185
r1   .21412994   .12560988
r1  -.00488978   .61970769
r1    .0026756   .72475307
r1   .03360286   .70664686
r1   .04963091   .56307451
r1   -.0588978   .48178213
r1  -.15129623   .04090772
r1   .05787663   .41487885
r1   -.0631449   .21101881
r1  -.03536931   .29799376
r1   .00051986    .9223438

. 
. foreach v of varlist own2008 maxcultage maxcultyears anypotatoes anyjyoti an
> ycmukhi areapotatoes2006 harvest2006 frachome2006 fracspoil2006 fracsold2006
>  price2006 fracphmlda2006 fracmkt2006 havelandline havecellphone asktrader o
> nlytrader askmarket askfriends askmedia dontsearch {
  2.  matrix `v'=J(1,2,0)
  3.  qui reg `v' mobilecontrol, clus(mzid)
  4.  matrix `v'[1,1]=_b[mobilecontrol]
  5.  test mobilecontrol
  6.  matrix `v'[1,2]=r(p)
  7.  }

 ( 1)  mobilecontrol = 0

       F(  1,    47) =    0.18
            Prob > F =    0.6748

 ( 1)  mobilecontrol = 0

       F(  1,    47) =    0.22
            Prob > F =    0.6442

 ( 1)  mobilecontrol = 0

       F(  1,    47) =    0.87
            Prob > F =    0.3563

 ( 1)  mobilecontrol = 0

       F(  1,    47) =    2.84
            Prob > F =    0.0987

 ( 1)  mobilecontrol = 0

       F(  1,    47) =    0.04
            Prob > F =    0.8440

 ( 1)  mobilecontrol = 0

       F(  1,    47) =    1.75
            Prob > F =    0.1923

 ( 1)  mobilecontrol = 0

       F(  1,    47) =    0.04
            Prob > F =    0.8330

 ( 1)  mobilecontrol = 0

       F(  1,    47) =    0.63
            Prob > F =    0.4322

 ( 1)  mobilecontrol = 0

       F(  1,    47) =    1.09
            Prob > F =    0.3022

 ( 1)  mobilecontrol = 0

       F(  1,    47) =    0.32
            Prob > F =    0.5715

 ( 1)  mobilecontrol = 0

       F(  1,    47) =    0.72
            Prob > F =    0.3995

 ( 1)  mobilecontrol = 0

       F(  1,    47) =    0.05
            Prob > F =    0.8316

 ( 1)  mobilecontrol = 0

       F(  1,    47) =    0.09
            Prob > F =    0.7657

 ( 1)  mobilecontrol = 0

       F(  1,    47) =    0.47
            Prob > F =    0.4978

 ( 1)  mobilecontrol = 0

       F(  1,    45) =    0.04
            Prob > F =    0.8330

 ( 1)  mobilecontrol = 0

       F(  1,    45) =    0.00
            Prob > F =    0.9941

 ( 1)  mobilecontrol = 0

       F(  1,    45) =    2.55
            Prob > F =    0.1176

 ( 1)  mobilecontrol = 0

       F(  1,    45) =    0.83
            Prob > F =    0.3678

 ( 1)  mobilecontrol = 0

       F(  1,    45) =    0.38
            Prob > F =    0.5425

 ( 1)  mobilecontrol = 0

       F(  1,    45) =    0.00
            Prob > F =    0.9515

 ( 1)  mobilecontrol = 0

       F(  1,    45) =    0.29
            Prob > F =    0.5923

 ( 1)  mobilecontrol = 0

       F(  1,    45) =    0.12
            Prob > F =    0.7316

.  
. matrix allmc = own2008 \ maxcultage \ maxcultyears \ anypotatoes \ anyjyoti 
> \ anycmukhi \areapotatoes2006 \ harvest2006 \ frachome2006 \ fracsold2006 \ 
> price2006 \ fracphmlda2006 \ fracmkt2006 \ havelandline \ havecellphone \ on
> lytrader \ asktrader \ askmarket \ askfriends \ askmedia \ dontsearch

. matrix list allmc

allmc[21,2]
            c1          c2
r1  -.04451454   .67478668
r1  -.57661825   .64422515
r1   .41275434   .35631774
r1   .01133425   .09870814
r1   .00463674   .84402034
r1   .06005827   .19233351
r1   .02875951   .83297092
r1   790.14023    .4321609
r1  -.00854103   .30223578
r1  -.02794642   .39953266
r1  -.03515732   .83161711
r1  -.00227715   .76565786
r1   .00411093   .49778207
r1   .01768972   .83298659
r1   -.0006268    .9941371
r1  -.08981971   .36783444
r1  -.13847949    .1176139
r1   .04720595   .54252082
r1   .00440502   .95146712
r1  -.02105007   .59232566
r1   .00238097   .73157768

. 
. foreach v of varlist own2008 maxcultage maxcultyears anypotatoes anyjyoti an
> ycmukhi areapotatoes2006 harvest2006 frachome2006 fracsold2006 price2006 fra
> cphmlda2006 fracmkt2006 havelandline havecellphone asktrader onlytrader askm
> arket askfriends askmedia dontsearch {
  2.  matrix `v'=J(1,2,0)
  3.  qui reg `v' villagemobile, clus(mzid)
  4.  matrix `v'[1,1]=_b[villagemobile]
  5.  test villagemobile
  6.  matrix `v'[1,2]=r(p)
  7.  }

 ( 1)  villagemobile = 0

       F(  1,    47) =    0.21
            Prob > F =    0.6527

 ( 1)  villagemobile = 0

       F(  1,    47) =    0.77
            Prob > F =    0.3846

 ( 1)  villagemobile = 0

       F(  1,    47) =    0.96
            Prob > F =    0.3327

 ( 1)  villagemobile = 0

       F(  1,    47) =    1.03
            Prob > F =    0.3163

 ( 1)  villagemobile = 0

       F(  1,    47) =    1.93
            Prob > F =    0.1717

 ( 1)  villagemobile = 0

       F(  1,    47) =    0.09
            Prob > F =    0.7632

 ( 1)  villagemobile = 0

       F(  1,    46) =    1.25
            Prob > F =    0.2703

 ( 1)  villagemobile = 0

       F(  1,    46) =    0.08
            Prob > F =    0.7780

 ( 1)  villagemobile = 0

       F(  1,    46) =    0.71
            Prob > F =    0.4040

 ( 1)  villagemobile = 0

       F(  1,    46) =    0.28
            Prob > F =    0.6006

 ( 1)  villagemobile = 0

       F(  1,    46) =    2.92
            Prob > F =    0.0941

 ( 1)  villagemobile = 0

       F(  1,    46) =    0.08
            Prob > F =    0.7807

 ( 1)  villagemobile = 0

       F(  1,    46) =    0.04
            Prob > F =    0.8464

 ( 1)  villagemobile = 0

       F(  1,    44) =    0.04
            Prob > F =    0.8513

 ( 1)  villagemobile = 0

       F(  1,    44) =    0.41
            Prob > F =    0.5249

 ( 1)  villagemobile = 0

       F(  1,    44) =    0.02
            Prob > F =    0.8844

 ( 1)  villagemobile = 0

       F(  1,    44) =    0.13
            Prob > F =    0.7222

 ( 1)  villagemobile = 0

       F(  1,    44) =    0.02
            Prob > F =    0.8949

 ( 1)  villagemobile = 0

       F(  1,    44) =    1.08
            Prob > F =    0.3047

 ( 1)  villagemobile = 0

       F(  1,    44) =    0.16
            Prob > F =    0.6932

 ( 1)  villagemobile = 0

       F(  1,    44) =    0.06
            Prob > F =    0.8122

.  
. matrix allvm = own2008 \ maxcultage \ maxcultyears \ anypotatoes \ anyjyoti 
> \ anycmukhi \ areapotatoes2006 \ harvest2006 \ frachome2006 \ fracsold2006 \
>  price2006 \ fracphmlda2006 \ fracmkt2006 \ havelandline \ havecellphone \ o
> nlytrader \ asktrader \ askmarket \ askfriends \ askmedia \ dontsearch

. matrix list allvm

allvm[21,2]
            c1          c2
r1   .06540074   .65274396
r1  -.87420412   .38460042
r1   .39032258   .33267319
r1   .00184843   .31630854
r1  -.05260351   .17165899
r1   .01557648   .76323471
r1   .19983518   .27029757
r1   454.70289   .77801948
r1   .00740957   .40401129
r1   .01767058   .60055455
r1   .24928726   .09405254
r1  -.00261264   .78073054
r1  -.00143533   .84641268
r1   .01591314   .85132308
r1   .05025771   .52486122
r1   .03092191    .7222197
r1  -.01281674   .88442503
r1   .01067069   .89493254
r1  -.06754992   .30468429
r1  -.01431925    .6931766
r1  -.00186111   .81217999

. 
. matrix all = allvc, allmc, allvm

. 
. logit villagecontrol own2008 maxcultage maxcultyears anypotatoes anyjyoti an
> ycmukhi areapotatoes2006 harvest2006 fracsold2006 price2006 fracphmlda2006 h
> avelandline havecellphone onlytrader askmarket askfriends, clus(mzid)

note: anypotatoes != 1 predicts failure perfectly
      anypotatoes dropped and 5 obs not used

Iteration 0:   log pseudolikelihood =  -353.6428  
Iteration 1:   log pseudolikelihood =  -332.2149  
Iteration 2:   log pseudolikelihood = -332.16667  
Iteration 3:   log pseudolikelihood = -332.16667  

Logistic regression                               Number of obs   =        514
                                                  Wald chi2(15)   =      17.63
                                                  Prob > chi2     =     0.2829
Log pseudolikelihood = -332.16667                 Pseudo R2       =     0.0607

                                  (Std. Err. adjusted for 46 clusters in mzid)
------------------------------------------------------------------------------
             |               Robust
villagecon~l |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     own2008 |  -.0741788   .1449404    -0.51   0.609    -.3582568    .2098992
  maxcultage |  -.0144384   .0084149    -1.72   0.086    -.0309313    .0020545
maxcultyears |   .0512876    .040994     1.25   0.211    -.0290591    .1316344
 anypotatoes |          0  (omitted)
    anyjyoti |  -.4892363   .7969052    -0.61   0.539    -2.051142    1.072669
   anycmukhi |   .6729232   .6578097     1.02   0.306    -.6163601    1.962206
areapot~2006 |   .0496544   .0437821     1.13   0.257    -.0361569    .1354657
 harvest2006 |   .0000314   .0000265     1.18   0.236    -.0000206    .0000834
fracsold2006 |  -.3893309   .8588693    -0.45   0.650    -2.072684    1.294022
   price2006 |   .0319567   .2136495     0.15   0.881    -.3867886     .450702
fracphm~2006 |   1.002072   .8794742     1.14   0.255    -.7216655     2.72581
havelandline |  -.0541528   .4747018    -0.11   0.909    -.9845512    .8762455
havecellph~e |   .2112657   .3480079     0.61   0.544    -.4708171    .8933486
  onlytrader |  -.6447127   .4717351    -1.37   0.172    -1.569297    .2798712
   askmarket |  -.0164158   .6168418    -0.03   0.979    -1.225404    1.192572
  askfriends |  -.6736595   .4792964    -1.41   0.160    -1.613063    .2657442
       _cons |  -.1889253   1.897291    -0.10   0.921    -3.907548    3.529697
------------------------------------------------------------------------------

. logit mobilecontrol own2008 maxcultage maxcultyears anypotatoes anyjyoti any
> cmukhi areapotatoes2006 harvest2006 fracsold2006 price2006 fracphmlda2006 ha
> velandline havecellphone onlytrader askmarket askfriends, clus(mzid)

Iteration 0:   log pseudolikelihood =  -364.5842  
Iteration 1:   log pseudolikelihood = -352.15295  
Iteration 2:   log pseudolikelihood = -352.06792  
Iteration 3:   log pseudolikelihood = -352.06754  
Iteration 4:   log pseudolikelihood = -352.06754  

Logistic regression                               Number of obs   =        529
                                                  Wald chi2(16)   =      19.27
                                                  Prob > chi2     =     0.2548
Log pseudolikelihood = -352.06754                 Pseudo R2       =     0.0343

                                  (Std. Err. adjusted for 46 clusters in mzid)
------------------------------------------------------------------------------
             |               Robust
mobilecont~l |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     own2008 |  -.0840928   .1688857    -0.50   0.619    -.4151027    .2469171
  maxcultage |   .0011627   .0098761     0.12   0.906    -.0181941    .0205195
maxcultyears |   .0380715   .0425474     0.89   0.371    -.0453199    .1214628
 anypotatoes |   1.129743   1.482757     0.76   0.446    -1.776406    4.035893
    anyjyoti |   .4875379   .8035094     0.61   0.544    -1.087312    2.062387
   anycmukhi |   .7670501   .6743978     1.14   0.255    -.5547453    2.088845
areapot~2006 |  -.0452867   .0684909    -0.66   0.508    -.1795264     .088953
 harvest2006 |   .0000279    .000028     1.00   0.319     -.000027    .0000827
fracsold2006 |  -.6352647   .8559613    -0.74   0.458    -2.312918    1.042389
   price2006 |  -.0947253   .2604481    -0.36   0.716    -.6051942    .4157436
fracphm~2006 |   .9298186    1.30636     0.71   0.477      -1.6306    3.490237
havelandline |   .0936174     .45094     0.21   0.836    -.7902088    .9774435
havecellph~e |  -.2253842   .3617781    -0.62   0.533    -.9344562    .4836878
  onlytrader |  -.4710694   .4871032    -0.97   0.334    -1.425774    .4836354
   askmarket |  -.0611423   .6729055    -0.09   0.928    -1.380013    1.257728
  askfriends |  -.2924759   .5295918    -0.55   0.581    -1.330457    .7455049
       _cons |  -1.951532   2.651021    -0.74   0.462    -7.147437    3.244373
------------------------------------------------------------------------------

. logit villagemobile own2008 maxcultage maxcultyears anypotatoes anyjyoti any
> cmukhi areapotatoes2006 harvest2006 fracsold2006 price2006 fracphmlda2006 ha
> velandline havecellphone onlytrader askmarket askfriends, clus(mzid)

note: anypotatoes != 1 predicts failure perfectly
      anypotatoes dropped and 1 obs not used

Iteration 0:   log pseudolikelihood = -326.38633  
Iteration 1:   log pseudolikelihood = -314.90563  
Iteration 2:   log pseudolikelihood = -314.63328  
Iteration 3:   log pseudolikelihood = -314.61088  
Iteration 4:   log pseudolikelihood = -314.61075  
Iteration 5:   log pseudolikelihood = -314.61075  

Logistic regression                               Number of obs   =        471
                                                  Wald chi2(15)   =      15.61
                                                  Prob > chi2     =     0.4082
Log pseudolikelihood = -314.61075                 Pseudo R2       =     0.0361

                                  (Std. Err. adjusted for 44 clusters in mzid)
------------------------------------------------------------------------------
             |               Robust
villagemob~e |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     own2008 |  -.1059518    .147141    -0.72   0.471    -.3943429    .1824393
  maxcultage |  -.0158997   .0078281    -2.03   0.042    -.0312425   -.0005569
maxcultyears |    .011776   .0394755     0.30   0.765    -.0655946    .0891466
 anypotatoes |          0  (omitted)
    anyjyoti |  -1.244998   1.074595    -1.16   0.247    -3.351165    .8611689
   anycmukhi |  -.1232547   .6290909    -0.20   0.845     -1.35625    1.109741
areapot~2006 |   .1243206   .1161849     1.07   0.285    -.1033976    .3520388
 harvest2006 |   .0000116   .0000233     0.50   0.618    -.0000341    .0000573
fracsold2006 |   .3285347   .8021478     0.41   0.682    -1.243646    1.900715
   price2006 |   .1445105   .2416714     0.60   0.550    -.3291568    .6181779
fracphm~2006 |   -.273111   1.198894    -0.23   0.820    -2.622901    2.076679
havelandline |  -.1655146   .4903296    -0.34   0.736    -1.126543    .7955137
havecellph~e |   .4761991    .310199     1.54   0.125    -.1317798    1.084178
  onlytrader |  -.2249395   .4789487    -0.47   0.639    -1.163662    .7137827
   askmarket |   .0956014   .6041226     0.16   0.874    -1.088457     1.27966
  askfriends |  -.4546551   .5511643    -0.82   0.409    -1.534917    .6256072
       _cons |   1.202357   2.255373     0.53   0.594    -3.218092    5.622806
------------------------------------------------------------------------------

. 
. *******************Appendix Table A3:
. use mandiprices_median, clear

. 
. foreach v of varlist d retailprice dxretail meanyield v_wagecashmale pctland
> line tubewellspv canalspv puccaroadpv indmillpv bankpv {
  2. ttest `v' if medpur==0, by(reltomedian)
  3. }

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
   below |       8    .5067286    .0197927    .0559823    .4599262    .5535309
   above |       6     .453777    .0451972    .1107101    .3375939    .5699601
---------+--------------------------------------------------------------------
combined |      14     .484035     .022585    .0845053    .4352431     .532827
---------+--------------------------------------------------------------------
    diff |            .0529515     .044975               -.0450405    .1509436
------------------------------------------------------------------------------
    diff = mean(below) - mean(above)                              t =   1.1774
Ho: diff = 0                                     degrees of freedom =       12

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.8691         Pr(|T| > |t|) = 0.2619          Pr(T > t) = 0.1309

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
   below |       8    4.914782           0           0    4.914782    4.914782
   above |       6    4.914782    2.29e-16    5.62e-16    4.914782    4.914782
---------+--------------------------------------------------------------------
combined |      14    4.914782    2.63e-16    9.85e-16    4.914782    4.914782
---------+--------------------------------------------------------------------
    diff |                   0    1.96e-16               -4.27e-16    4.27e-16
------------------------------------------------------------------------------
    diff = mean(below) - mean(above)                              t =   0.0000
Ho: diff = 0                                     degrees of freedom =       12

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.5000         Pr(|T| > |t|) = 1.0000          Pr(T > t) = 0.5000

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
   below |       8     249.046    9.727695    27.51408    226.0437    272.0484
   above |       6    223.0215    22.21343    54.41157    165.9201    280.1229
---------+--------------------------------------------------------------------
combined |      14    237.8927    11.10003    41.53252    213.9125    261.8728
---------+--------------------------------------------------------------------
    diff |            26.02453    22.10423               -22.13644     74.1855
------------------------------------------------------------------------------
    diff = mean(below) - mean(above)                              t =   1.1774
Ho: diff = 0                                     degrees of freedom =       12

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.8691         Pr(|T| > |t|) = 0.2619          Pr(T > t) = 0.1309

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
   below |       8     10.5827    .0936857    .2649831    10.36117    10.80424
   above |       6     10.0816    .2716972    .6655194    9.383177    10.78002
---------+--------------------------------------------------------------------
combined |      14    10.36794    .1399968    .5238202     10.0655    10.67039
---------+--------------------------------------------------------------------
    diff |            .5011079    .2564627               -.0576762    1.059892
------------------------------------------------------------------------------
    diff = mean(below) - mean(above)                              t =   1.9539
Ho: diff = 0                                     degrees of freedom =       12

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.9628         Pr(|T| > |t|) = 0.0744          Pr(T > t) = 0.0372

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
   below |       8    56.04167    3.524539      9.9689    47.70746    64.37588
   above |       6    53.40404    4.203912    10.29744    42.59754    64.21054
---------+--------------------------------------------------------------------
combined |      14    54.91126    2.620389    9.804598    49.25025    60.57226
---------+--------------------------------------------------------------------
    diff |            2.637626    5.458452                -9.25532    14.53057
------------------------------------------------------------------------------
    diff = mean(below) - mean(above)                              t =   0.4832
Ho: diff = 0                                     degrees of freedom =       12

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.6812         Pr(|T| > |t|) = 0.6376          Pr(T > t) = 0.3188

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
   below |       8    .0685728    .0040301    .0113989    .0590431    .0781025
   above |       6    .0973903    .0115752    .0283534    .0676352    .1271453
---------+--------------------------------------------------------------------
combined |      14    .0809232    .0065366    .0244578    .0668017    .0950446
---------+--------------------------------------------------------------------
    diff |           -.0288175    .0109455               -.0526658   -.0049692
------------------------------------------------------------------------------
    diff = mean(below) - mean(above)                              t =  -2.6328
Ho: diff = 0                                     degrees of freedom =       12

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0109         Pr(|T| > |t|) = 0.0219          Pr(T > t) = 0.9891

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
   below |       8    95.45833      10.567    29.88799    70.47135    120.4453
   above |       6    99.29293    11.57406    28.35055    69.54086     129.045
---------+--------------------------------------------------------------------
combined |      14    97.10173     7.53097    28.17831    80.83206    113.3714
---------+--------------------------------------------------------------------
    diff |           -3.834596     15.8007               -38.26136    30.59217
------------------------------------------------------------------------------
    diff = mean(below) - mean(above)                              t =  -0.2427
Ho: diff = 0                                     degrees of freedom =       12

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.4062         Pr(|T| > |t|) = 0.8123          Pr(T > t) = 0.5938

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
   below |       8    .7291667    .0992906    .2808363    .4943816    .9639517
   above |       6    .7146465    .0791577     .193896    .5111651    .9181278
---------+--------------------------------------------------------------------
combined |      14    .7229437    .0637985     .238712    .5851155    .8607719
---------+--------------------------------------------------------------------
    diff |            .0145202    .1341179               -.2776976     .306738
------------------------------------------------------------------------------
    diff = mean(below) - mean(above)                              t =   0.1083
Ho: diff = 0                                     degrees of freedom =       12

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.5422         Pr(|T| > |t|) = 0.9156          Pr(T > t) = 0.4578

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
   below |       8    .6041667    .0941625    .2663316    .3815079    .8268255
   above |       6    .5770202    .1120626    .2744962    .2889541    .8650863
---------+--------------------------------------------------------------------
combined |      14    .5925325     .069369    .2595551    .4426698    .7423951
---------+--------------------------------------------------------------------
    diff |            .0271465     .145689               -.2902826    .3445755
------------------------------------------------------------------------------
    diff = mean(below) - mean(above)                              t =   0.1863
Ho: diff = 0                                     degrees of freedom =       12

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.5724         Pr(|T| > |t|) = 0.8553          Pr(T > t) = 0.4276

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
   below |       8       .5625    .1132909     .320435    .2946097    .8303903
   above |       6    .5378788     .149161    .3653683    .1544482    .9213094
---------+--------------------------------------------------------------------
combined |      14    .5519481    .0873385    .3267908    .3632647    .7406314
---------+--------------------------------------------------------------------
    diff |            .0246212    .1835561               -.3753132    .4245557
------------------------------------------------------------------------------
    diff = mean(below) - mean(above)                              t =   0.1341
Ho: diff = 0                                     degrees of freedom =       12

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.5522         Pr(|T| > |t|) = 0.8955          Pr(T > t) = 0.4478

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
   below |       8    .0416667    .0272772    .0771517   -.0228337    .1061671
   above |       6    .0719697    .0235946    .0577946    .0113179    .1326215
---------+--------------------------------------------------------------------
combined |      14    .0546537    .0183848    .0687896    .0149357    .0943716
---------+--------------------------------------------------------------------
    diff |            -.030303    .0376651               -.1123683    .0517622
------------------------------------------------------------------------------
    diff = mean(below) - mean(above)                              t =  -0.8045
Ho: diff = 0                                     degrees of freedom =       12

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.2184         Pr(|T| > |t|) = 0.4367          Pr(T > t) = 0.7816

. 
. foreach v of varlist d retailprice dxretail meanyield v_wagecashmale pctland
> line tubewellspv canalspv puccaroadpv indmillpv bankpv {
  2. ttest `v' if medpur==1, by(reltomedian)
  3. }

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
   below |       5    3.217131    .0539196    .1205678    3.067426    3.366836
   above |       3    3.229517    .0481249    .0833547    3.022453    3.436582
---------+--------------------------------------------------------------------
combined |       8    3.221776     .035939    .1016507    3.136794    3.306758
---------+--------------------------------------------------------------------
    diff |           -.0123861    .0800236               -.2081967    .1834246
------------------------------------------------------------------------------
    diff = mean(below) - mean(above)                              t =  -0.1548
Ho: diff = 0                                     degrees of freedom =        6

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.4410         Pr(|T| > |t|) = 0.8821          Pr(T > t) = 0.5590

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
   below |       5    7.774921    5.25e-16    1.17e-15    7.774921    7.774921
   above |       3    7.774921    3.63e-16    6.28e-16    7.774921    7.774921
---------+--------------------------------------------------------------------
combined |       8    7.774921    3.36e-16    9.50e-16    7.774921    7.774921
---------+--------------------------------------------------------------------
    diff |                   0    7.49e-16               -1.83e-15    1.83e-15
------------------------------------------------------------------------------
    diff = mean(below) - mean(above)                              t =   0.0000
Ho: diff = 0                                     degrees of freedom =        6

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.5000         Pr(|T| > |t|) = 1.0000          Pr(T > t) = 0.5000

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
   below |       5    2501.294    41.92204    93.74054      2384.9    2617.688
   above |       3    2510.924    37.41669     64.8076    2349.933    2671.915
---------+--------------------------------------------------------------------
combined |       8    2504.905    27.94225    79.03263    2438.832    2570.978
---------+--------------------------------------------------------------------
    diff |           -9.630111    62.21769               -161.8713    142.6111
------------------------------------------------------------------------------
    diff = mean(below) - mean(above)                              t =  -0.1548
Ho: diff = 0                                     degrees of freedom =        6

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.4410         Pr(|T| > |t|) = 0.8821          Pr(T > t) = 0.5590

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
   below |       5    9.796386    .2939777    .6573541    8.980173     10.6126
   above |       3     9.05332    .4652335     .805808    7.051582    11.05506
---------+--------------------------------------------------------------------
combined |       8    9.517736    .2693374    .7618012    8.880855    10.15462
---------+--------------------------------------------------------------------
    diff |            .7430664    .5187259                 -.52621    2.012343
------------------------------------------------------------------------------
    diff = mean(below) - mean(above)                              t =   1.4325
Ho: diff = 0                                     degrees of freedom =        6

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.8990         Pr(|T| > |t|) = 0.2020          Pr(T > t) = 0.1010

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
   below |       5    52.73833     4.15769    9.296876    41.19474    64.28193
   above |       3    55.55555     4.36562    7.561475    36.77181     74.3393
---------+--------------------------------------------------------------------
combined |       8    53.79479    2.912292    8.237205    46.90832    60.68127
---------+--------------------------------------------------------------------
    diff |           -2.817222    6.394994               -18.46521    12.83077
------------------------------------------------------------------------------
    diff = mean(below) - mean(above)                              t =  -0.4405
Ho: diff = 0                                     degrees of freedom =        6

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.3375         Pr(|T| > |t|) = 0.6750          Pr(T > t) = 0.6625

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
   below |       5    .0310375    .0193816    .0433385   -.0227743    .0848494
   above |       3    .0551069    .0456642    .0790927   -.1413703    .2515842
---------+--------------------------------------------------------------------
combined |       8    .0400635    .0194158    .0549161   -.0058475    .0859746
---------+--------------------------------------------------------------------
    diff |           -.0240694    .0421892               -.1273027    .0791639
------------------------------------------------------------------------------
    diff = mean(below) - mean(above)                              t =  -0.5705
Ho: diff = 0                                     degrees of freedom =        6

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.2945         Pr(|T| > |t|) = 0.5890          Pr(T > t) = 0.7055

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
   below |       5    9.290909    5.798557    12.96597   -6.808466    25.39028
   above |       3    22.12222    16.05973    27.81627   -46.97722    91.22167
---------+--------------------------------------------------------------------
combined |       8    14.10265     6.71972    19.00624   -1.786962    29.99227
---------+--------------------------------------------------------------------
    diff |           -12.83131     14.0474               -47.20405    21.54143
------------------------------------------------------------------------------
    diff = mean(below) - mean(above)                              t =  -0.9134
Ho: diff = 0                                     degrees of freedom =        6

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.1981         Pr(|T| > |t|) = 0.3962          Pr(T > t) = 0.8019

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
   below |       5        .225    .1952562    .4366062   -.3171182    .7671182
   above |       3    .4777778    .2696591    .4670633   -.6824718    1.638027
---------+--------------------------------------------------------------------
combined |       8    .3197917    .1534486    .4340182   -.0430566    .6826399
---------+--------------------------------------------------------------------
    diff |           -.2527778    .3264348               -1.051535    .5459794
------------------------------------------------------------------------------
    diff = mean(below) - mean(above)                              t =  -0.7744
Ho: diff = 0                                     degrees of freedom =        6

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.2341         Pr(|T| > |t|) = 0.4681          Pr(T > t) = 0.7659

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
   below |       5    .1227273    .0772727    .1727871   -.0918162    .3372708
   above |       3    .0666667    .0666667    .1154701   -.2201769    .3535102
---------+--------------------------------------------------------------------
combined |       8    .1017045    .0520955    .1473484   -.0214818    .2248909
---------+--------------------------------------------------------------------
    diff |            .0560606    .1139544               -.2227759    .3348971
------------------------------------------------------------------------------
    diff = mean(below) - mean(above)                              t =   0.4920
Ho: diff = 0                                     degrees of freedom =        6

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.6799         Pr(|T| > |t|) = 0.6402          Pr(T > t) = 0.3201

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
   below |       5    .5606061    .1344137    .3005581    .1874139    .9337982
   above |       3    .4222222    .1309981    .2268953    -.141417    .9858614
---------+--------------------------------------------------------------------
combined |       8    .5087121     .094511    .2673174    .2852292     .732195
---------+--------------------------------------------------------------------
    diff |            .1383838    .2031537               -.3587154    .6354831
------------------------------------------------------------------------------
    diff = mean(below) - mean(above)                              t =   0.6812
Ho: diff = 0                                     degrees of freedom =        6

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.7394         Pr(|T| > |t|) = 0.5212          Pr(T > t) = 0.2606

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
   below |       5    .2462121    .0860274    .1923631    .0073618    .4850624
   above |       3          .2    .0693889     .120185   -.0985562    .4985562
---------+--------------------------------------------------------------------
combined |       8    .2288826    .0568374    .1607604    .0944835    .3632816
---------+--------------------------------------------------------------------
    diff |            .0462121    .1253983               -.2606264    .3530506
------------------------------------------------------------------------------
    diff = mean(below) - mean(above)                              t =   0.3685
Ho: diff = 0                                     degrees of freedom =        6

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.6374         Pr(|T| > |t|) = 0.7251          Pr(T > t) = 0.3626

. 
. *******************Appendix Table A4: See above the code for Tables 6 & 7
. 
end of do-file

